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The study proposes efficient controllers to regulate frequency in highly penetrated power systems by renewable energy sources. Frequency controllers for variable speed wind turbines are designed to extract the stored energy in rotating masses, and efficiently regulate the pitch angle for frequency support. A temporary fast participation of storage battery incorporated with photovoltaic sources is provided via auxiliary controller. Controllers' fine tuning is realised by using stochastic fractal optimiser (SFO). The integral time absolute error in area frequency and tie line power represents the adapted objective function subjects to set of constraints. The performance assessments are carried out in three phases: (i) at initial stage, the secondary control is disabled and impact of wind penetration level on frequency nadir and frequency deviations are investigated, (ii) coordinated inertia/energy storage control is demonstrated, and (iii) finally, robustness analysis is made considering system parametric variations and real weather data. The proposed control strategy is verified by simulation in MATLAB/SIMULINK environment. The drawn numerical results by the SFO are compared with those achieved by genetic algorithm and the built-in controller tuner in SIMULINK. Performance assessments, comparative study along with robustness analysis of the SFO results confirm its viability and effectiveness.

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Efficient modelling of photovoltaic (PV) generating units' characteristics to investigate their steady-state and dynamic impacts on the performances of power systems and electric drives is essential. The current work aims at developing an effective tool based on artificial ecosystem optimiser (AEO) to define (optimally) the uncertain parameters of PV generating units. The root mean squared deviations (RMSDs) along with the predefined inequality constraints formulate the optimization problem to be solved by the AEO. Initially, two test cases with different PV technologies are demonstrated complete with their relevant discussions and necessary validations. At a later stage, real measurements (followed the procedures of IEC 60904) of a commercial PV module namely Ultra 85-P of Shell Power-Max are made for further experimental validations of the AEO results. Various operating temperatures and sun irradiance levels are investigated among the simulated scenarios. The statistical validations along with predefined indices plus comparisons to other competing methods appraise the results obtained by the AEO. It can be confirmed that the AEO is able to produce best values of unidentified parameters of the PV units with different solar technologies under study with lesser values of RMSDs among other optimisers. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Braking of three phase induction motors is required in many industrial applications. This paper introduces braking of three phase induction motors using particle swarm optimization (PSO) technique. The objective is to determine the optimum values of the applied voltage and frequency during braking to stop the motor in a certain time with minimum braking energy losses to limit any excessive thermal heating. The proposed technique is important and more useful in applications of repeated braking cycles. The results are compared with that obtained using plugging braking method and it's found that the proposed technique gives lower braking energy and shorter braking time. The braking energy losses with the proposed method are about 20% of the plugging braking energy losses with the same braking time. The proposed method determines the variation of optimal values of applied voltage and frequency to have a certain braking time of three phase induction motor at a certain load torque with minimum braking energy losses. The characteristics of the motor are simulated using SIMULINK/MATLAB.

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