Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photo Voltaic Systems

Abstract

The efficiency of the low-cost renewable energy source i.e. solar is very poor due to inadequate extraction of maximum power. By employing the proper maximum power point tracking algorithm, the efficiency can be increased. An innovative adaptive backstepping neural network controller is proposed in this paper to extract the maximum power from the solar panels by tracking the desired photovoltaic array voltage in real-time. The maximum power is extracted indirectly by tuning the PV voltage to the desired PV voltage where the maximum power is attained at the desired PV voltage point. The desired photovoltaic array voltage is obtained from the linear regression method. The change in photovoltaic current caused by varying irradiance and temperature is approximated using the Chebyshev polynomials. The quicker steady-state and transient responses are accomplished and the computational burden of the photovoltaic system control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of the neurons in accordance with the Lyapunov stability analysis. Also, Lyapunov control function of the backstepping control design procedure finds a control law by an innovative cubic equation interpretation, instead of resolving the first derivative of the control law, that diminishes the ripples in the duty cycle to make its appropriateness in real-time. A prototype is developed to validate the robustness of this controller in maximum power extraction at a faster time and the results confirm that adaptive backstepping neural network controller surpasses the performances of conventional backstepping controller and constant voltage PID controller.

Low-voltage and high-penetration distributed photovoltaic access control technology based on intelligent measurement

“…Under uniform load distribution, the photovoltaic power supply is distributed and connected to Nodes 3, 6, 8, and 22. Using the methods in [3] and [4] as comparison methods, the comparison results of the maximum allowable capacity output are shown in Figure 2. From Figure 2, it can observe the maximum acceptance capacity of each node, and the capacity values between nodes in this method have a small difference from the actual maximum admission capacity.…”

“…In [3], a maximum power point tracking control algorithm suitable for photovoltaic power generation systems is established, utilizing adaptive backstepping neural networks to achieve maximum output of photovoltaic modules. In [4], a distribution network scheduling method that combines new energy vehicles with solar cells is studied.…”

Implementation of sliding mode backstepping controller for boost converter in real‐time for LED application

“…The second error variable Substitute Equation () in Equation () By substituting Equation () in Equation () In order to produce an asymptotically stable system, the Lyapunov function candidate is chosen as (Govindharaj et al, 2021): By differentiating the above Lyapunov function in equ. (), can be obtained as Taking the differentiation of equ.…”

“…In order to produce an asymptotically stable system, the Lyapunov function candidate is chosen as (Govindharaj et al, 2021):…”

“…The Lyapunov function is chosen, and the control law is obtained to get the asymptotically stable system. The backstepping controller's main drawback is that it cannot attain the desired value for any uncertainty with reduced time domain (transient and steady‐state) performances (Govindharaj & Mariappan, 2020; Govindharaj et al, 2021).…”