1 / 10

Embedded Implementation of Power System Monitoring Algorithms

Embedded Implementation of Power System Monitoring Algorithms. Raymond McNamara , 09505075 Electrical Energy Systems FYP Presentation , January 2013. Introduction. Develop & implement numerous algorithms in real-time for monitoring and control of power systems.

oneida
Download Presentation

Embedded Implementation of Power System Monitoring Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Embedded Implementation of Power System Monitoring Algorithms Raymond McNamara, 09505075 Electrical Energy Systems FYP Presentation, January 2013

  2. Introduction • Develop & implement numerous algorithms in real-time for monitoring and control of power systems. • Artificial signal generation on Matlab. • Compare filter-bank approaches with spectral analysis approaches.(Performance and complexity) • Port and evaluate the algorithm to a suitable real-time embedded platform. • Develop & evaluate functionality for a suitable closed-loop control algorithm in Matlab. • Port & evaluate closed-loop control algorithm to real-time embedded platform.

  3. Research • Lookingatlimits of class C equipment(Lightingequipment) • Accuracy of 1% replicatingthat of the ADE7880 EnergyMeter Reference: www.ieee.li

  4. Filter Bank Approach Fast Fourier Transform method

  5. Notch Filter • Added to remove the peakat the first harmonic component with magnitude 1. >>freqz(Numerator Coefficients, Denominator Coefficients) Transfer function for the filter:

  6. Second order system IIR filter(Resonator) • Filterseachharmonicseparately. • Removes gain. • First 2000 samplesremoved due to filterimplementation. Transfer function for the filter: 50 Hz Gain removed =1 1000 Hz 1950Hz

  7. Fast Fourier transform Method • Zero-paddingwithnextnearest power of 2 greaterthan the number of original samples ( 66536 instead of 51000).

  8. Performance and Computational Complexity • Assuming 5 seconds and 51000 samples. (5 x 51000) = 255,000. • Notch & IIR Filter – 6 & 4 multiplies and 4 & 2 adds.(1 & 39 harmonicsrespectively) =1 (255,000x6)+39(255,000x4)mul & 1 (255,000x4) & 39(255,000x2)adds. • Total = 41310000 + 20910000= 62,220,000. • FFT and inverse= 2(2Nlog2N) = 18,319,340 • Multiplication : 4N = 1,020,000 • Total = 19339340. • Savingof 68.9% with FFT

  9. Future Plans • Sort out Zero-paddingwithin the FFT to make the algorithim more efficient with the DSP chip. • Select a DSP chip thatwill have the capability of handling the data. • Hopefully all goingwell, implement a closed control loop to monitor and adjust.

  10. Conclusion • For futher information about the project: http://harmonicalgorithm.wordpress.com/ • Thankyou for your time and I hopeyou have enjoyed the presentation. • AnyQuestions?

More Related