How to Achieve Operational Excellence in Wind Farms

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The global renewable energy industry has seen unprecedented growth over the last several years. According to the Global Wind Energy Council (GWEC), the cumulative installed capacity of wind power projects has increased from 24 GW in 2001 to 432 GW in 2015 and is expected to grow to 703 GW by 2020 (Global Data). Much of this growth has resulted from great state and national level policies, tax incentives and high electricity prices. However, many of these incentives have since been reduced and now wind power companies face pressure to improve profitability, while scaling operational excellence. Mechanical engineering and physics based improvements have long been used to increase plant operations. However, these improvements plateau after a period of time thus providing diminishing returns. In the increasingly connected world of today, sensors are being used to achieve operation excellence based on real time, plant specific data. Companies now are realizing the value of using data to maximize returns as compared to making any physic improvements to the components. In this day and age of the Internet of Things (IoT), sensors are being used to record data from every device, and the renewable energy industry is not one to be left behind.

Governments around the world are slowly scaling down renewable energy incentives such as tax benefits and generation based incentives while renewable energy electricity prices are falling. Companies are thus focusing on minimizing operations and maintenance (O&M) costs and maximizing power production. Data analytics enables O&M teams to achieve both goals by taking a closer look at the data from every plant device. According to a recent report by McKinsey, depending upon the plant’s existing level of performance, better O&M could account for as much as a 20% increase in IRR. The type of data analytics for better wind farm O&M falls into three categories: a) forecasting, b) alarms based on threshold values, and c) condition monitoring. Continue reading →

MachinePulse Will Be At RE-Invest 2015 In New Delhi On Feb. 15-17

Posted by MachinePulse Tech Pvt. Ltd.

The MachinePulse team will be at the first Renewable Energy Global Investors Meet & Expo (RE-Invest 2015) on February 15-17 in New Delhi. Visit us at stall number 76 to learn about our Internet of Things solutions for the renewable energy industry.

RE-Invest is organized by the Ministry of New and Renewable Energy (MNRE), Government of India as a follow-up to the ‘Make in India’ initiative launched by the Prime Minister of India. The central theme of RE-Invest is to attract large scale investments for the renewable energy sector in India. RE-Invest signals India’s commitment to the development and scaling up of renewable energy to meet its energy requirements. The event is expected be attended by over 200 investors and over 1000 delegates, both domestic and international. Additionally, representatives from the State Government, Public Sector Enterprises, renewable power developers and manufacturers, state renewable energy nodal agencies, and other related stakeholders will play important roles.

To schedule a meeting with us at RE-Invest, please email sales@machinepulse.in

About MachinePulse
MachinePulse™ is an Internet of Things platform provider based in Mumbai. The big data platform, erixis™, has in-built machine learning and predictive analytics capabilities. MachinePulse is based in Mumbai, India with branch offices in Chennai and Bangalore. Contact us to learn more.

Machine Learning Techniques For Better And More Efficient Solar Power Plants

Posted by MachinePulse Tech Pvt. Ltd.

Machine learning techniques support better solar power plant forecasting.
Machine learning techniques play a crucial role in deciding where to build a plant when accurate or limited location data is available.
Machine learning techniques help maintain smart grid stability.

The global solar photovoltaic (PV) installed capacity in 2013 was 138.9 GW and it is expected to grow to over 455 GW by 2020. However, solar power plants still have a number of limitations that prevent it from being used on a larger scale. One limitation is that the power generation cannot be fully controlled or planned for in advance since the energy output from solar power plants is variable and prone to fluctuations dependent on the intensity of solar radiation, cloud cover and other factors. Another important limitation is that solar energy is only available during the day and batteries are still not an economically viable storage option making careful management of energy generation necessary. Additionally, as the installed capacity of solar power plants grows and plants are increasingly installed at remote locations where location data is not readily available, it is becoming necessary to determine their optimal sizes, locations and configurations using other methods. Machine learning techniques provide solutions that have been more successful in addressing these challenges than manually developed specialized models.

Accurate forecasts of solar power production are a necessary factor in making the renewable energy technology a cost-effective and viable energy source. Machine learning techniques can correctly forecast solar power plant generation at a better rate than current specialized solar forecasting methods. In a study conducted by Sharma et al, multiple regression techniques including least-square support vector machines (SVM) using multiple kernel functions were used in the comparison with other models to develop a site specific prediction model for solar power generation based on weather parameters. Experimental results showed that the SVM model outperformed the others with up to 27 percent more accuracy.

Furthermore, machine learning techniques play a crucial role in assisting decision making steps regarding the plants location selection and orientation selection as solar panels need to be faced according to solar irradiation to absorb the optimal energy. Conventional methods for sizing PV plants have generally been used for locations where the required weather data (irradiation, temperature, etc.) and other information concerning the site is readily available. However, these methods cannot be used for sizing PV systems in remote areas where the required data are not available, and thus machine learning techniques are needed to be employed for estimation purposes. In a study conducted by Mellit et al., an artificial neural network (ANN) model was developed for estimating sizing parameters of stand-alone PV systems. In this model, the inputs are the latitude and longitude of the site, while the outputs are two hybrid-sizing parameters. In the proposed model, the relative error with respect to actual data does not exceed 6 percent, thus providing accurate predictions. This model has been evaluated on 16 different sites and experimental results indicated that prediction error ranges from 3.75-5.95 percent with respect to the sizing parameters. Additionally, metaheuristic search algorithms address plan location optimization problems by providing improved local searches under the assumption of a geometric pattern for the field.

Lastly, to maintain grid stability, it is necessary to forecast both short term and medium term demand for a power grid with renewable energy sources contributing a considerable proportion of energy supply. The MIRABEL system offers forecasting models which target flexibilities in energy supply and demand, to help manage the production and consumption in the smart grid. The forecasting model combines widely adopted algorithms like SVM and ensemble learners. The forecasting model can also efficiently process new energy measurements to detect changes in the upcoming energy production or consumption. It also employs different models for different time scales in order to better manage the demand and supply depending on the time domain.

Ultimately, machine learning techniques support better operations and management of solar power plants.

How Predictive Analytics Can Make the Renewable Energy Industry Grow

Posted by MachinePulse Tech Pvt. Ltd.

Renewable energy technologies are quickly gaining acceptance globally as a reliable source of electricity. Total global renewable energy installations have increased from 160 GW in 2004 to more than 1,560 GW in 2013. With a growing installed capacity of renewable energy plants comes a growing number of remote monitoring solutions to track the performance of these plants. Enormous amounts of data are being generated by these renewable energy plants and it is becoming ever important to create valuable insights from this data. Big data analytics performed on the data collected from these plants, enables owners and O&M crews to operate the renewable plants at the plants maximum potential. Among all the types of big data analytics that could be performed on the plant data, predictive analytics holds the most promising of providing insights by leveraging performance data to create correlations and outcomes. Let us understand how it could impact on Renewable Energy Industry.

“Data is not Information, Information is not Knowledge and Knowledge is not Wisdom”.

From data to decision science (Source: infogineering)

There are multiple steps required to reach from data collection to generating actionable insights. Predictive analytics is the link in this chain that takes us from the ‘Information’ stage to ‘knowledge’ stage. It models the cause and effect relationship among various parameters using various data mining techniques, statistical models and machine learning techniques thus allowing us a window to see the contextual future events.

According to a study, 20% – 40% of renewable energy cannot be used because it is unstable.

Predictive analytics when used deftly on renewable energy power plants can provide accurate energy production forecasts. It also predicts the machine breakdowns or glitches thereby optimizing overall operational efficiencies. The analytics checks for the correlation of various parameters like irradiation, wind speed, temperature, humidity, cloud cover, transformer status etc. and learns their cause and effect relationship. One study estimates that a good predictive model can increase the power generating capacity of a wind farm by about 10%, which practically revitalizes the entire business. It is also important to note that Predictive Analytics doesn’t only improve operational efficiencies but also improves the life span of the valuable renewable energy technology assets.

Banks are yet to consider renewable energy projects as a sound investment compared to oil and gas power projects.

The current growth of renewable energy technologies could be amplified if there is enough data to prove that they are credible investment options. Numerous renewable energy power projects still lack appropriate funding because of the lack of historic data that raises suspicions on the long term viability of the projects. Predictive analytics can addresses this problem by accurately forecasting energy generation based on historic performance, weather and other parameters. These quantifiable results associated with revenues generated from the future performance can improve the bankability of renewable energy projects.

Ultimately, predictive analytics is set to provide immense value to the renewable energy industry. It is now up to the plants owners to capitalize on this statistical tool to achieve the most out of their renewable energy power plant.

MachinePulse to demo SolarPulse at IIT-Bombay on Nov. 28, 2014

Posted by MachinePulse Tech Pvt. Ltd.

MachinePulse will give a live demonstration of SolarPulse at IIT-B on Nov. 28, 2014

MachinePulse will host a live demonstration of SolarPulse, our solar power plant remote monitoring and control solution, on November 28, 2014 at IIT-Bombay’s campus as part of a short-term course entitled ‘Solar PV Modules and System Testing and Characterization’. The three day course is jointly organized by the Continuing Education Program of IIT Bombay and the National Centre for Photovoltaic Research and Education (NCPRE).

During the period of this course, students will be given the opportunity to visit the 1 megawatt (MW) solar photovoltaic (PV) plant installed at the IIT-Bombay campus. Niketan Shitole, System Integration Engineer at MachinePulse will guide students through the different components of the 1 MW solar power plant. After the plant visit, Niketan will host an exclusive live demo of SolarPulse by logging into IIT-Bombay’s 1 MW solar power plant account. During this live demo, students will view the plant’s live generation and performance data. Niketan will also explain the parameters used to measure a solar power plant’s performance, track past performance and the process followed to respond to plant related alerts and alarms. Students will also be introduced to iceBOX™, MachinePulse’s smart hardware device that collects the data generated from solar power plants, aggregates it and sends it to the server where it is analysed and the end result is presented on the SolarPulse dashboard.

The course begins on November 26, 2014 and ends on November 28, 2014. On completing the course, students can expect to gain an intermediate level understanding of test and characterization techniques, the types of solar photovoltaic (PV) modules, balance of systems components, PV systems and the technical complexities of a solar PV remote monitoring and control software.

To learn more about the event please click here to visit the NCPRE site.

MachinePulse is at the 8th annual Renewable Energy India Expo

Posted by MachinePulse Tech Pvt. Ltd.

MachinePulse will be at the 8th annual Renewable Energy India (REI) Expo! The event will take place from September 3-5, 2014 at the India Expo Centre in Greater Noida, India. Come visit us at stall number 2.44 on the second floor to learn more about our machine data solutions and how we can help increase your investments in your renewable energy power plants.

REI, Sept. 3-5

REI brings together decision makers and influencers as well as technical experts and professionals from leading companies involved in the renewable energy generation, transmission and distribution within India and around the world.

We look forward to meeting with you soon!