Better Technology Better World

The economic viability of energy conservation solutions becomes increasingly evident when consideration is given to today’s industrial environment. As population and economies grow and the living standard increases, the demand for ever increasing amounts of energy increases with no end in sight.  At the same time, public alarm continues to rise because of global warming emissions and the ever augmenting demand for more energy. 

 

The OLCMD offers an attractive way to facilitate the transition to optimized power generation systems. It accomplishes increased yield and reduces investment in several different ways.  It eliminates waste and delivers performance.  The combination of its inherent profitability traits of optimization is further amplified by the ever increasing need for technologies that mitigate environmental impacts and the qualities that the OLCMD broad application power generation system has to offer over prior art: unmatched simplicity, flexibility and high quality optimization.  Because of these traits  and since the OLCMD is a broad application technology, it can be more readily applied to power generation system niches that have not used optimization or could further benefit from OLCMD optimization.  For example it can be applied to variable speed air conditioning systems maximizing efficiency while increasing its energy saving ability.

 

Different types of power generation system optimizers have emerged as the demand for more energy efficient power generation systems has increased.  Power generation system optimizers can be designed using two basic configurations: using either a feedforward configuration or using a feedback configuration. 

 

Feedforward optimizers typically obtain information about environmental factors affecting performance using information from sensors that is digitized and put into a complex algorithm along with other system information obtained from lookup tables. 

The information is input into the mathematical algorithm which calculates new setting values of parameters that affect the performance of the power generation system, power transfer parameters (PTPs), so that the power generation system adapts by maximizing power and efficiency to a changing environment.  Because of their complexity, feedforward optimizers are more costly, less flexible and usually applied in dedicated applications

 

The problem with conventional feedforward systems that find and integrate the ideal setting values of power transfer parameters in a power generation system, like with any predictive system, is that modeling an environment using sensors tends to be very complex and is dependent on how accurate the model is.  In pursuit of attaining a more complete and accurate model, more sensors may be used to make the model more complete thereby increasing the cost and complexity of the system.  Optimizing more PTPs may also be a way of increasing optimization performance, though this also vastly increases its complexity because the ideal setting value of one power transfer parameter also affects the ideal setting value of the other power transfer parameters in the power generation system. 

 

For example, if two power transfer parameter (PTP) wind turbine one that integrates the setting values of rpm/torque and pitch to maximize efficiency,  is then upgraded to a three PTP system, one that also charges a battery where the new optimizer needs to have means to set the ideal value of the rate of charge of this battery, the third PTP, while also determining how this will influence the system as a whole including the ideal setting values of the other two power transfer parameters: turbine pitch and generator rpm/torque ratio.  This represents a vast increase the energy optimizer’s algorithm’s complexity. It is important to realize that as an optimizer system becomes more complex, it costs more to make and maintain. It also becomes less flexible and more dedicated to the system for which it was intended.  It becomes less viable.

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Like many good patented inventions, the Optimal Load Controller Method and Device (OLCMD) is based on a solid foundation of proven ideas.  The OLCMD started from the “maximum power transfer theorem” of electrical engineering regarding the transfer of power in resistive electronic circuits.  Some of the other ideas would include that optimization must be based on considering the power generation system as a whole and that all the factors affecting power generation system output converge on power output.  Therefore, in the OLCMD adaptive power generation system optimizer, all factors are considered based on sampling system power output and making comparisons based on measuring system power output at different instances in time, thereby vastly simplifying the problem of adaptive integrated power generation system optimization.  This of course makes the OLCMD a very attractive power generation system optimizer.

 

Because the OLCMD adaptive power generation system is based on making a comparison of power output measurements originating from the same place, any sensor drift is neutralized because both samples have the same sensor drift.  This smart configuration has eliminated one of the hardest problems: how to predict sensor drift.  This configuration also adapts to changes in the power generation system performance due to wear and tear or other changes, such as a change in octane, because all factors converge on system power output. 

 

At Innovative Technologies Corporation, our continuing achievements are due not to luck or easy circumstance but are the product of hard work and tenacity driven by passion and a sense of purpose that our technology is not only economically significant for energy providers and users, but also of great importance to mitigating climate change and maintaining a healthy global environment. 

 

Innovative Technologies Corporation is offering the OLCMD patented  technology under license agreements to further develop OLCMD applications in different energy sectors to make a better world environment.