Design of Malignant Load Identification and Control System

  • Wei Li
  • Tian ZhouEmail author
  • Xiang Ma
  • Bo Qin
  • Chenle Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


The potential security risks existing in high-power illegal electric appliances is a problem that university power management faces. The traditional electrical identification system has disadvantages of low accuracy, high complexity and high hardware cost. In order to solve this problem, a malignant load intelligent identification and control system based on SoC (RN8302) chip combined with STM32F105 processor is designed, moreover, the principles of algorithm and hardware circuit are given in detail. The real-life test proves that the system has accurate measurement and high load identification rate. The RN8302 chip-based intelligent load identification and control system has certain practical application value for its simple design and low cost.


RN8302 STM32F105 Intelligent identification 



This work was supported by Shanxi Province Technical Innovation Guide Special project (2018SJRG-G-03). This work also supported by Shanxi education department industrialization project (16JF024).


  1. 1.
    Ying, C.: Design of intelligent detection system for illegal electric appliances with malicious load in campus power grid. Sci. Technol. Bull. 29(4), 61–63 (2013). Scholar
  2. 2.
    Cui, J., Li, P.: Design of high precision intelligent electric meter based on ATT7022B. Electron Technol. 23(2), 46–48 (2010). Scholar
  3. 3.
    Liu, M., Bo, H.: Design and implementation of a new comprehensive monitoring linkage function model. Autom. Instrum. 27(11), 31–34 (2012). 10.19-557/j.cnki.1001-9944.2012.11.009Google Scholar
  4. 4.
    Chen, W., Deng, X., Lu, T.: Design and implementation of a new power grid voltage monitor based on STC12C5A32AD. Instrumentation 20(9), 41–43 (2009).
  5. 5.
    Du, J., Wan, S., Zhu, Z.: Research on the auxiliary decision-making function of integrated monitoring system based on case reasoning. J. Qingdao Univ. 26(4), 39–42 (2011). 10.1330-6/j.10069798.2011.04.008Google Scholar
  6. 6.
    Linna, W., Meng, X.: A new sinusoidal signal distortion evaluation method. J. Electron. Meas. Instrum. 19(3), 67–71 (2005). Scholar
  7. 7.
    Chen, D., Han, J.: MATLAB based design method for fixed-point DSP wavelet transform program. Data Acquis. Process. 21(5), 86–89 (2006). 10.163-37/j.1004-9037.2006.s1.040Google Scholar
  8. 8.
    Zhao, C., He, M.: Harmonic detection algorithm based on complex wavelet transform phase information. China J. Electr. Eng. 1(25), 38–40 (2005). Scholar
  9. 9.
    Su, Y., Jade, W.: Modeling of non-contact power supply phase-shifting control systems. J. Electron. Technol. 23(7), 92–97 (2008). 0.19595/j.cnki.1000-6753.tces.2008.07.016Google Scholar
  10. 10.
    Zhao, W., Chen, S., Lu, W.: Research on intelligent identification methods of malignant load. Sci. Technol. Sq. 3(3), 48–50 (2014). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Li
    • 1
  • Tian Zhou
    • 1
    Email author
  • Xiang Ma
    • 1
  • Bo Qin
    • 1
  • Chenle Zhang
    • 1
  1. 1.Xi’an University of Posts and TelecommunicationsXi’anChina

Personalised recommendations