Micro Wind Power Generator with Battery Energy Storage for Critical Load



In the micro-grid network, it is especially difficult to support the critical load without uninterrupted power supply. The proposed micro-wind energy conversion system with battery energy storage is used to exchange the controllable real and reactive power in the grid and to maintain the power quality norms as per International Electro-Technical Commission IEC- 61400-21 at the point of common coupling. The generated micro wind power can be extracted under varying wind speed and can be stored in the batteries at low power demand hours. In this scheme, inverter control is executed with hysteresis current control mode to achieve the faster dynamic switchover for the support of critical load. The combination of battery storage with micro-wind energy generation system (μWEGS), which will synthesize the output waveform by injecting or absorbing reactive power and enable the real power flow required by the load. The system reduces the burden on the conventional source and utilizes μWEGS and battery storage power under critical load constraints. The system provides rapid response to support the critical loads. The scheme can also be operated as a stand-alone system in case of grid failure like a uninterrupted power supply. The system is simulated in MATLAB/SIMULINK and results are presented.


  1. Battery energy storage
  2. Micro-wind energy generating system
  3. Power quality



Fig. 1. Scheme of micro-wind generator with battery storage for critical load application.


 Fig. 2. (a) Source current. (b) Inverter injected current. (c) Load current.

 Fig. 3. (a) Source current. (b) Load current. (c) Inverter-injected current.

Fig. 4. (a) DC link voltage. (b) Rectified current of wind generator.

(c) Current supplied by battery. (d) Charging-discharging of dc link capacitor.

Fig. 5. Source current and source voltage at PCC.

Fig. 6. (a) Source current. (b) FFT of source current.

Fig. 7. (a) Source current. (b) FFT of source current.

Fig. 8. Active and reactive power (a) at source, (b) load, and (c) inverter.


In this project, modeling of bi-directional DC-DC converter is developed for wind energy generation and simulated in MATLAB/SIMULINK. The performance of the bi-directional converter using triangle PWM technique has been analyzed from the prospective of input/output characteristics and harmonic content of output voltage and current. The multi-stage current charging method is used to charge the batteries. At various wind speeds, the system can use the battery for energy storage to keep the load voltage and load current stable. Control strategy and system design can be easily implemented and able to improve the efficiency of wind turbine systems.


[1] Kusiak, A., Zhang, Z. and Li, M.Y. (2010) Optimization of Wind Turbine Performance with Data-Driven Models. IEEE Transactions on Sustainable Energy, 1, 66-76. http://dx.doi.org/10.1109/TSTE.2010.2046919

[2] Tita, I. and Calarasu, D. (2009) Wind Power Systems with Hydrostatic Transmission for

Clean Energy. Environmental Engineering and Management Journal , 8, 327-334.

[3] Quaschning, V. (2005) Understanding Renewable Energy Systems. Earthscan, London.

[4] Kazimierczuk, M.K. and Czarkowski, D. (1993) Application of the Principle of Energy Conservation to Modeling the PWM Converters. Second IEEE Conference on Control Applications , 13-16 September 1993, 291-296. http://dx.doi.org/10.1109/cca.1993.348274

[5] Miao, Z. and Fan, L. (2012) Modeling and Small Signal Analysis of a PMSG Based Wind Generator with Sensor Less Maximum Power Extraction. 2012 IEEE Power and Energy Society General Meeting , 22-26 July 2012, 1-8.


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The current electric grid is an inefficient system that wastes significant amounts of the electricity it produces because there is a disconnect between the amount of energy consumers require and the amount of energy produced from generation sources. Power plants typically produce more power than necessary to ensure adequate power quality. By taking advantage of energy storage within the grid, many of these inefficiencies can be removed. When using battery energy storage systems (BESS) for grid storage, advanced modeling is required to accurately monitor and control the storage system. A battery management system (BMS) controls how the storage system will be used and a BMS that utilizes advanced physics-based models will offer for much more robust operation of the storage system.

BESSs require a battery management system (BMS) to monitor and maintain safe, optimal operation of each battery pack and a system supervisory control (SSC) to monitor the full system. Batteries are dynamic in nature, constantly operating outside the equilibrium state during cycling. In addition, the situation worsens for the case of intercalation-based storage systems (e.g., Li chemistry) which operate as a closed system with very few measurable state variables, making it difficult to properly monitor the states of the battery and maintain safe operation. Furthermore, even under normal operation the battery packs of a BESS will degrade during cycling. This degradation can be accelerated by extreme charging patterns, increased temperature (both ambient and operating), overcharging, or undercharging. A basic BMS controls battery packs only to meet the power demand. However, smarter model-based BMSs can reduce the causes of degradation and improve the performance of the system. Predictive and adaptive BMSs based on models are especially important for large battery packs for applications such as electric vehicles and grid integration.

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