A Fast and Effective Control Algorithm for Maximum Power Point Tracking in Wind Energy Systems

With the increased interest in using environmentally friendly sources of energy, wind energy has become a popular research topic. Although the availability of wind is unpredictable, wind power is capable of supplying hundreds of megawatts of power. As a result, wind energy is considered as a valuable supplemental power source. The main challenge associated with wind energy conversion is the wasted potential wind power due to fluctuating wind conditions. With each change in wind velocity, the system must be corresponding adjusted to its optimum operating point to allow maximum power transfer. This paper presents a new adaptive control algorithm for maximum power point tracking (MPPT) for wind energy systems to minimize the loss of potential power. Under fluctuating wind velocities and air density, the algorithm can capture maximum power by using its estimation process, modified hill climb searching (HCS) method, and its adaptive memory. The algorithm’s flexible and trainable memory allows it to determine the turbine’s natural behaviour so that its optimum point determination process speeds up over time. As the algorithm is trained, the algorithm continually adapts to its given turbine by establishing the characteristics of the system during system operation. Consequently, this results in more accurate wind variation estimations. With the algorithm logic and estimation process, it is capable of determining the optimum operating point quite quickly even before training. In addition, the algorithm does not require extensive information regarding the turbine characteristics, it can be applied to any given wind turbine. A simulated front-end rectifier wind energy system has been built in PSIM 7.0 for the verification of the proposed algorithm. The electrical system consists of a permanent magnet synchronous generator, diode rectifier, and a boost converter with power factor correction (PFC). The algorithm is realized in C++ script. Detailed descriptions of the proposed control algorithm will be provided for illustration purposes. Simulation results have shown that the algorithm is able to determine the optimum operating point under changes in wind speed and can successfully adapt to a turbine. It has also been shown that subsequent optimal point determination speeds up over time as a result of the adaptive nature of the algorithm.