ANN Peak Load Shaver.

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1

2 ANN Peak Load Shaver.

3 ANN Peak Load Shaver.

4 ANN Based Electricity Load Forecasting By: Ameya Deoras [1] For short term operations and long term planning for utilities, accurate forecasts are critical. Data used for this example is obtained form New England pool region [2].

5 ANN Based Electricity Load Forecasting By: Ameya Deoras [1]

6 ANN Peak Load Shaver.

7 By: Jiaqi Liang et al. [3]

8

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10 Case 1: Load & Cap trip in Bus 5

11 Case 2: Line 2-5 Outage

12 Case 3: Large short-term varying loads at buses 4 &5.

13 AGC By: J.G. Ghodekar [4] AGC is used to maintain the nominal frequency of the interconnected power system. AGC is used to keep the power flow between the different parts of the power system at the predefined values.

14 AGC Problem In a control area, all the generators speed up or slow down together to maintain the frequency and relative power angles to scheduled values in static as well as dynamic conditions. A load perturbation creates power mismatch in generation and demand. This mismatch is initially compensated by an extraction of kinetic energy from the system, which causes a declining system frequency

15 AGC Problem If the mismatch is large enough, the governors come into action and the output of generators is increased primary control. Although the amount of kinetic energy extracted from the system is reduced to a greater extent, the decline in frequency still exists. PI Controller is needed to bring back the frequency to the nominal value Secondary control.

16 AGC Problem In the interconnected control areas, the control system of each area needs information about the dynamics in all other areas to restore the nominal values of area frequency and tie line powers. The information about each area is found in its output frequency and the information about other areas is in the deviation of tie line powers

17 AGC Problem AGC scheme for an interconnected power system basically incorporates suitable control system, which can bring the area frequencies and tie line powers back to nominal or very close to nominal values effectively after the load perturbations.

18 Drawbacks of conventional PI Slow action. Linear. Retuning the controller as per the change of the operating point of the system What is the value of the new gain? So a smarter controller is needed!

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24 ANN with power system in training

25 ANN with the power system as a Controller

26 ANN with the power system as a Controller

27 ANN with the power system as a Controller with incomplete states

28 ANN with the power system as a Controller with incomplete states

29 Electricity Load Forecast. Dynamic Stochastic Optimal Power Flow. ANN Peak Load Shaver. Transmission Line Protection.

30 ANN Peak Load Shaver with scheme of Fuel cell / µ turbine By: M. Soliman [5] The rapid increase in demand. Capacity proliferating falls behind Some unexpected fault makes the demand exceeds the available power. Distributed generation is to build and operate several generation units near load.

31 ANN Peak Load Shaver with scheme of Fuel cell / µ turbine By: M. Soliman [5] The objective is to use neural networks to control the flow of real and reactive power from a DG system consisting of a microturbine operating in parallel with a fuel cell when supplying an isolated load and sharing the load with the grid during peak periods.

32 ANN Peak Load Shaver with scheme of Fuel cell / µ turbine By: M. Soliman [5]

33 ANN Peak Load Shaver with scheme of Fuel cell / µ turbine By: M. Soliman [5] - Inputs - Active and reactive power - Battery current and q component of the inverter. - Outputs - Modulation index - Output voltage phase angle.

34 ANN Peak Load Shaver with scheme of Fuel cell / µ turbine ANN Peak Load Shaver with scheme of Fuel cell / µ turbine By: M. Soliman [5] 2.4 x NN control 2.2 Output real power (W) PID control Time (s)

35 ANN Peak Load Shaver with with scheme of scheme Fuel cell / of µ turbine Fuel cell / µ turbine By: M. Soliman [5]

36 ANN Peak Load Shaver with with scheme of scheme Fuel cell / of µ turbine Fuel cell / µ turbine By: M. Soliman [5]

37 Electricity Load Forecast. Dynamic Stochastic Optimal Power Flow. ANN Peak Load Shaver. Transmission Line Protection.

38 ANN Based Protection System for Controllable Series compensated TL By: A. Hosny [7] FACTS Pros To increase power transfer capability. To improve stability. To reduce transmission losses. Better voltage regulation. Oscillations damping. To mitigate subsynchronous resonance.

39 ANN Based Protection System for Controllable Series compensated TL By: A. Hosny [7] Protection becomes challenging task. Abrupt change in the TL impedance. Switching transients due to resonance between series cap. and power system inductance. Conventional Protection systems are likely to malfunction under such situation.

40 ANN Based Protection System for Controllable Series compensated TL By: A. Hosny [7] A reliable ANN-based protection system can be used to identify and locate faults on a transmission line with TCSC This system uses only the power system voltage and current samples.

41 The Power System Model 160 mile TL

42 The Power System Model

43 The Power System Model

44 Feature Extraction and Patterns Generation The power system state is determined through identifiable patterns of associated voltage and/or current waveforms. The features that contain sufficient information are needed to distinguish between classes and permit efficient computations.

45 Feature Extraction and Patterns Generation Through extensive fault studies: Fault before TCSC Exponentially decaying DC component. High frequency components Due to resonance with line inductance. Fundamental frequency component. Fault loop includes TCSC All the previous components and Sub-harmonics Due to resonance with system inductance.

46 Feature Extraction and Patterns Generation Fault location relative to the TCSC can be determined from the relative phase angles of the voltage and current phasors and current magnitudes, as well. The NN-based locator uses samples of the voltage and current signals and tries to learn the relationships that may exist in training patterns.

47 ANN-Based Protection System ANN FC Assign +1 if the line is involved in a fault. Assign -1 otherwise. Example { } 2 phase to ground (a-c-g) ANN FL Assign +1 if TCSC is in the fault loop. Assign -1 if TCSC is not in the fault loop.

48 Fault Classification ANN

49 Fault Classification ANN

50 Fault Location ANN

51 Fault Location ANN 20 mile

52 Fault Location ANN 100 mile

53 References [1] A. Deoras. (15 Sep 2011). Electricity Load and Price Forecasting Webinar Case Study. Available: [2] (2011). ISO-New England Marketing Data. Available: [3] L. Jiaqi, et al., "Adaptive critic design based dynamic optimal power flow controller for a smart grid," in Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on, 2011, pp [4] J. G. Ghodekar, "Automatic generation control of interconnected power systems using artificial neural network techniques," Ph.D., Bharath University, [5] M. Soliman, et al., "Dynamic analysis of microturbine/fuel cell for peak power shaving," in Power Engineering Society General Meeting, IEEE, 2006, p. 13 pp. [6] M. Soliman, et al., "Modeling of Fuel Cell/microturbine generation scheme with battery storage," in Electric Power and Energy Conference (EPEC), 2010 IEEE, 2010, pp [7] A. Hosny and M. Safiuddin, "ANN-based protection system for Controllable Series- Compensated transmission lines," in Power Systems Conference and Exposition, PSCE '09. IEEE/PES, 2009, pp. 1-6.

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