Hybrid k-means Grasshopper Optimization Algorithm based FOPID controller with feed forward DC–DC converter for solar-wind generating system

The main issue in renewable energy generating system is the variation in power generation due to the intermittent nature of the renewable sources. Due to this, voltage deviation and unexpected surges in the output voltage may affect the power system and cause unstable operation. Therefore, a power flow control of solar-wind Renewable Energy System (RES) is presented in this paper to reduce the power fluctuation and at the same time to maintain the State of Charge (SOC) of the battery within allowable limits. Initially, Maximum Power Point Tracking (MPPT) algorithm is used to operate the power system close to the peak power point. Then, the buck–boost converter with Feed Forward (FF) technique is employed to produce the response with lesser ripples. The Fractional Order Proportional Integral Derivative (FOPID) controller has the characteristics of having short rise time, reduced oscillations, or overshoot with strong robustness compared with the conventional PID controller. However, improper tuning of the controller parameters may degrade the performance of the system. Hence, we introduce a k-means Grasshopper Optimization Algorithm (k-GOA) to determine the best tuning parameters with faster convergence. In order to validate the supremacy of this technique, its performance is compared with the existing controllers like Jaya Optimization (JO) FOPID, Salp Swarm Optimization (SSO) FOPID, Chaotic Atom Search Optimization (ChASO) FOPID, Ant Lion Optimization (ALO) FOPID and Fibonacci Search Technique (FST) FOPID controllers. The outcomes show that despite sudden load changes and changes in the power generation, the power balance between the supply and demand is effectively managed by the proposed k-GOA FOPID controller. Moreover, the k-GOA has obtained faster convergence than existing optimization techniques and the converter have produced less ripples. Moreover, the proposed system has attained an efficiency of 98.5% whereas, the existing systems has obtained lower efficiency.