The output power of wind and photovoltaic (PV) systems contain fluctuations due to the intermittent nature of solar irradiance and wind speed. It is required to smooth the fluctuated power before supplying it to the grid. Here, a fuzzy‐based discrete Kalman filter approach is proposed for smoothing output power fluctuations of the wind and PV generation systems using a battery energy storage system. The proposed approach incorporates the state of health of the battery as a feedback to not only obtain smooth output power but also improve the battery health by adaptively regulating the battery power. The effectiveness of the proposed approach is verified by evaluating the power fluctuation rates and performance indices.

The battery energy storage system (BESS) is a popularly used source to smooth the power fluctuation caused by variation of wind speed and solar irradiance. The forecasted data of wind and solar irradiance may contain bias error and does not provide the true power available from wind and photovoltaic generating systems. This study proposes a method based on discrete Kalman filter (DKF) to eliminate the bias error present in the forecasted data so that true power of wind and photovoltaic systems could be predicted. A DKF‐based state‐space approach is utilised to predict the true power obtained through wind and photovoltaic generating systems. The proposed approach also attenuates the power fluctuation of wind and PVGSs considering the desired power dispatched to the load as well as evaluates the BESS power needed for this purpose.

In this paper, a modified two degree of freedom-internal model control structure is proposed with dual feedback loop configuration for load frequency control problem of the single area and two area non-reheated power systems to achieve better transient and steady state performance. Predictive model of the proposed configuration is derived through Routh, Padé and SOPDT approximations. Hence, for all three cases, the proposed configuration is simulated considering the single area and two area power systems with non-reheated turbine along with 50% parametric uncertainty. Finally, a comparison with existing techniques is included to validate the effectiveness and performance of the proposed control configuration through responses and error indices. It is observed that the proposed scheme generates better results under parameter variations.

In this paper, we propose model reduction algorithms based on the frequency-domain interval Gramians for 1-D and separable denominator 2-D discrete-time systems using balanced truncation as a parameterized combination of unweighted and the limited-frequency interval Gramians. The values of free parameters are computed using a line search optimization. The proposed algorithms provide a substantial improvement in the approximation error than the well-known existing techniques and generate stable reduced models along with an easily computable error-bound. The effectiveness of proposed algorithms is validated with the help of numerical examples of a sixth-order elliptic low-pass filter and a (6, 6)-order Roesser model of a separable denominator 2-D system.