Electrical Power Quality Enhancement of Grid Interfaced with Wind Power System Using STATCOM – Control Scheme

Electrical Power Quality Enhancement of Grid Interfaced with Wind Power System Using STATCOM – Control Scheme

ABSTRACT:

Infusion of the wind power into an electric grid influences the power quality. The exhibition of the wind turbine in this way power quality are resolved based on guidelines and the standards followed by the rule indicated in International Electro-technical Commission standard, IEC-61400. The impact of the wind turbine in the grid connected wind energy generation system are the active power, reactive power, voltage variations, harmonic distortion, flicker. The paper study exhibits the power quality issues due to establishment of wind turbine with the grid. In this proposed paper, STATCOM (Static Synchronous Compensator) is connected at point of common coupling (PCC) with a battery energy storage system (BESS) to reduce the power quality issues. The STATCOM control scheme for the grid associated wind energy generation system for power quality improvement is simulated utilizing MATLAB/SIMULINK. The viability of the proposed control scheme reduces reactive power from the load and induction generator. The advancement of the grid coordination rule and the plan for development in power quality standards as per IEC-standard on the grid has been introduced.

INDEX TERMSPower Quality, Renewable Energy, PCC (Power of Common Coupling), STATCOM (Static Synchronous Compensator), BESS (Battery Energy Storage System).

 SOFTWARE: MATLAB/SIMULINK

CONCLUSION:

The paper analyses the elements which influences the power quality in the wind energy generation system. Likewise this paper examines the execution of STATCOM-Control scheme for power quality improvement in grid associated wind energy generation system. The simulation of the proposed control scheme for the grid associated Wind energy generation is simulated utilizing MATLAB/SIMULINK. The control scheme has an ability to dispense with the harmonic parts of the load current and reactive power. Total Harmonic Distortion before the STATCOM connected was observed to be 24.62%, whereas, after STATCOM connection it was observed to be 2.54%. It additionally assists with keeping up the source voltage and current in-stage which makes maintaining power factor at source-end and thus supporting the demanding reactive power injection for the load at PCC and wind generator in the grid interfaced wind energy generation system. It allows an opportunity to upgrade the use factor of transmission lines.

 REFERENCES:

  • W Mohod, M.V Aware, ―A STATCOM control scheme for grid connected wind energy system for power quality improvement,‖ IEEE System Journal, Vol.2, issue 3, pp.346-352, Sept.2010
  • Yang, Student Member, IEEE, C. Shen, L. Zhang, M. L. Crow, and S.Atcitty, “Integration of a StatCom and Battery Energy Storage “-IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 2, MAY 2001.
  • Tatsuto Kinjyo, Tomonobu Senjyu, Katsumi Uezato, Hideki Fujita, and Toshihisa Funabashi, “Output Levelling of Wind Energy Conversion System by Current Source ECS” – IEEE Power Engineering Society General Meeting, 2004.
  • Kyungi Soo KOOK, Yilu LIU, Stan ATCITTY “Mitigation of the Wind Generation Integration Related Power Quality Issues by Energy Storage.”- Electrical Power Quality and Utilization, journal Vol.XII, no.2, 2006.
  • Kinjo. T and Senjyu. T, “Output leveling of renewable energy by electric double layer capacitor applied for energy storage system,” IEEE Trans. Energy Conv., vol. 21, no. 1, Mar. 2006

Performance Analysis of ANN Based three-phase four-wire Shunt Active Power Filter for Harmonic Mitigation under Distorted Supply Voltage Conditions

Abstract

This paper presents the design and implementation of three-phase four-wire shunt active power filter (SAPF). It consists of insulated gate bipolar transistors, IGBT based current-controlled voltage source inverter (CC-VSI), series coupling inductor and self-supported DC bus. Power electronics based converters and non-linear loads generate waveform-driven power quality issue as harmonics.Three-phase four-wire SAPF mitigates harmonics, compensates for reactive power, neutral current and power factor correction. Conventionally, the positive sequence detection control strategy using phase-locked loop (PLL) is applied as the synchronizing unit vector element to generate reference source currents. Conventional controller tuning process is difficult and fails to perform satisfactorily under supply voltage variation conditions. In this paper, Levenberg-Marquardt back propagation training algorithm based artificial neural network (ANN) controller is proposed to regulate DC link voltage due to its self-adapting and rapid calculation characteristics that allow the controller to handle high nonlinearity and uncertainty in a non-linear system. Weights of a neuron are adapted to minimize total harmonic distortion (THD) of source current under the step, ramp, time series amplitude variation and frequency and amplitude of modulation conditions. The proposed system is modelled in MATLAB/SIMULINK environment and laboratory prototype with dSpace1104 control card is developed. Experimentation results validate the simulated results of the proposed scheme under supply voltage variations for three-phase four-wire distribution system.

 Conclusion

An artificial neural network based controller for three phase four-wire shunt active power filter is designed and developed for harmonic elimination, improved transient performance, convergence and reduced computational burden. It is used to regulate DC-link voltage and neutral current compensation to mitigate harmonics. Indirect current control has been described for the proposed system. Simulated results show that the proposed controller is capable of adopting itself during large voltage variations. It is trained by LMBS algorithm to minimize harmonics. The performance of the proposed system is demonstrated through MATLAB/Simulink simulation. A dSpace1104 based laboratory prototype has been developed. Experimental results are in close vicinity to demonstrate and validate simulation results. Results have been analysed under abnormal utility conditions. Spectral analysis has defined THD level of compensated source current as IEEE519 standards under step, ramp and time series voltage amplitude variation and amplitude and frequency modulation.

References

  1. H. Akagi, “New trends in active filters for power conditioning,” IEEE Trans. Ind. Appl., Vol. 32, no. 6, pp. 1312–22, Nov. 1996.
  2. Bhim Singh, Kamal Al-Haddad, and Ambrish Chandra, “A review of active filters for power quality improvement,” IEEE Trans. Ind. Electron., Vol. 46, no. 5, pp. 960–71, Oct. 1999.
  3. M. EI Habrouk, M. K. Darwish, and P. Mehta, “Active power filters: A review,” IEE Proc. Electr. Power Appl., Vol. 147, no. 5, pp. 403–13, 2000.
  4. Gitanjali Mehta, and Sajjan Pal Singh, “Power quality improvement through grid integration of renewable energy sources,” IETE. J. Res., Vol. 59, no. 3, pp. 210–18, 2013.
  5. Mikkili Suresh, Anup Kumar Panda, and Y. Suresh, “Fuzzy controller based 3 phase 4 wire shunt active filter for mitigation of current harmonics with combined p-q and Id-Iq control strategies,” Energy Power Eng., Vol. 3, no. 1, pp. 43–52, Feb. 2011.

ANN Based MPPT Applied To Solar Powered Water Pumping System Using BLDC Motor

 

ABSTRACT

This paper introduces non-electrical input based artificial neural network (ANN) maximum power point tracking (MPPT) technique to the solar powered water pumping system using brushless DC (BLDC) motor. The objective is to model a step size independent MPPT using neural network for water pumping application. A DC-DC boost converter is being utilized which is driven by ANN based MPPT to extract maximum power out of solar photovoltaic (SPV) array. And also responsible for soft starting of BLDC motor. Pulse width modulated (PWM) control of the voltage source inverter (VSI) using DC link voltage controller is used to control the speed of the BLDC motor. PWM signal is generated using the inbuilt encoder to perform the electronic commutation by hall signal sensing. Performance analysis of a BLDC motor driving pump system is carried out under the MATLAB/Simulink environment. And efficiency of the overall system is calculated under various irradiance conditions.

SOFTWARE: MATLAB/SIMULINK

CONCLUSION:

In this paper, a non-electrical input-based ANN MPPT is introduced for solar power water pumping system using BLDC motor. The objective was to introduce a step size independent MPPT technique and optimal modeling of the system. The outcomes have demonstrated that usage of ANN-based MPPT is one of conceivable option design step size independent operation of PV array driving water pumping system using BLDC motor. It has been observed that the system has excellent transient and steady-state performance over a wide range of irradiance. Results have proven the optimal performance of the system with the highest efficiency of 81.55% and maintain a continuous flow of water even at the lowest irradiance with an efficiency of 69.03%. Soft starting of BLDC motor is also achieved using a proposed method which is desirable for smooth operation of the motor pump set.

REFERENCES:

  1. Rajan Kumar and Bhim Singh , “BLDC motor driven water pump fed by solar photovoltaic array using boost converter,” in Annual IEEE India (INDICON), New Delhi, 2015.
  2. Bhim Singh and Ranjan Kumar, “Solar PV Array Fed Brushless DC Motor Driven water pump,” in IEEE 6th International Conference on Power Systems (ICPS), New Delhi, 2016.
  3. Subudhi and R. Pradhan,, “A comparative study on maximum power point tracking techniques for photovoltaic power systems,” IEEE Trans. Sustain. Energy,, vol. 4, no. 1, pp. 89-98, jan 2013.
  4. Lina M. Elobaid, Ahmed K. Abdelsalam and Ezeldin E. Zakzouk, “Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey,” IET Renewable Power Generation, 9, no. 8, pp. 1043-63, 2015.
  5. Najet Rebei, Rabiaa Gammoudi , Ali hmidet and Othman Hasnaoui, “Experimental Implementation Techniques of P&O MPPT Algorithm for PV Pumping System,” in IEEE 11th International Multi-Conference on Systems, Signals & Devices, Barcelona, Spain,

Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network

ABSTRACT:

Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take place in a motor. Stator related faults are among the most commonly occurring faults in BLDC motor. With an initial raise in local heating, a fault in the stator can largely reduce motor efficiency and account for the entire system breakdown. In this study, we present a deep learning-based approach to estimate the remaining useful life (RUL) of BLDC motor affected by different stator related faults. To analyze the motor health degradation, we have investigated two types of stator faults namely inter-turn fault (ITF) and winding short-circuit fault (WSC). A generator was coupled with the motor and using an average value rectifier (AVR), generator’s output voltage was monitored for the entire lifecycle. A proven neural network for effective sequence modeling, recurrent neural network (RNN) is selected to train the voltage degradation data. For a better estimation of nonlinear trends, long-short term memory (LSTM) with attention mechanism is chosen to make predictions of the motor RUL for both types of faults. The main concern that encourages authors of this paper is the proposed method can be used for the real-time condition monitoring and health state estimation of BLDC motors. Also, the proposed AVR-LSTM method is not affected by environmental influences, making it suitable for diverse operating conditions.

KEYWORDS:

  1. Attention mechanism
  2. BLDC motor
  3. Remaining useful life
  4. LSTM
  5. Stator fault

SOFTWARE: MATLAB/SIMULINK

CONCLUSION:

BLDC motor has gained vast popularity over a few decades due to its high efficiency and low maintenance. With increased demand and complex operation environment, a robust prognostics and health management framework for BLDC motors is essential. This paper has presented an effective RUL estimation method of BLDC motor by considering the generator output voltage as a health indicator. Two types of stator related faults are investigated namely ITF fault and WSC fault. MCSA is performed on motor current for both fault types to understand the fault characteristics and identify the faults at the earliest stage. To acquire generator output voltage, we have used an average value rectifier to efficiently sense and acquire data. Collected data for the entire lifecycle are normalized using moving average filtering and ground truth of the degradation is obtained as true RUL. Later, a conventional LSTM model and attention-based LSTM model were trained for the future predictions of RUL. Proposed attention LSTM model is found to be more effective in predicting RUL for both types of faults. Outcomes of this paper can be summarized as below: (1) This RUL estimation method can be used for different operating conditions as it is free from environmental influences such as- heat, noise, and vibration. (2) This method will allow us to predict the RUL as well as estimate the state-of-health of motor during operation. This real-time condition monitoring technique will be highly applicable for the BLDC motor’s condition monitoring on the fly. (3) The model build for the prediction of output voltage can be further implemented for active power monitoring, efficiency monitoring, etc. Since these measures also depend on the output voltage and current, real-time condition monitoring can be performed using these indexes keeping the motor in operation. In future works, we look forward to modeling uncertainties associated with this RUL estimation framework. The proposed AVR-LSTM model with attention mechanism can be further modified to capture the fault-related information using a real-time updating method. This will further increase the robustness and reliability of this method.

REFERENCES:

[1] M. Pecht and M. Kang, Prognostics and Health Management of Electron- ics: Fundamentals, Machine Learning, and the Internet of Things. Chich-ester, U.K.: Wiley, Aug. 2018, pp. 1_130, doi: 10.1002/9781119515326.

[2] N. H. Kim, D. An, and J. H. Choi, Prognostics and Health Management of Engineering Systems An Introduction. Cham, Switzerland: Springer, 2017, pp. 127_241, doi: 10.1007/978-3-319-44742-1.

[3] G. Vachtsevanos, F. L. Lewis, M. Roemer, A. Hess, B.Wu, Intelligent Fault Diagnosis and Prognosis for Engineering System. Hoboken, NJ, USA: Wiley, 2006.

[4] A. Heng, S. Zhang, A. C. C. Tan, and J. Mathew, “Rotating machinery prognostics: State of the art, challenges and opportunities,” Mech. Syst. Signal Process., vol. 23, no. 3, pp. 724_739, Apr. 2009.

[5] S. Kumar, D. Mukherjee, P. K. Guchhait, R. Banerjee, A. K. Srivastava, D. N. Vishwakarma, and R. K. Saket, “A comprehensive review of condition based prognostic maintenance (CBPM) for induction motor,” IEEE Access, vol. 7, pp. 90690_90704, 2019.