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In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems; the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator's opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator; in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator's opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator's opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.

In this study, a fuzzy current‐sensor less maximum power point tracking (MPPT) algorithm is presented. The algorithm proposed to improve the performance and reduce the cost of photovoltaic systems. The proposed algorithm creates the variable step‐size/variable frequency specification in the drift‐free single input voltage sensor (SIVS) method designing a reliable and widely used fuzzy control algorithm. The proposed algorithm will improve the SIVS methods employing the advantages of the fuzzy controllers in the MPPT algorithms based on the real and false error areas analysis concept developed by Tousi et al. (2016). The proposed method will detect exact drift conditions and move to the optimal setpoint at the maximum speed. The method selects the correct direction, appropriate step‐size, and suitable control frequency to increase dynamic efficiency against the drift condition. Also, the steady‐state efficiency of the algorithm will increase because of high‐frequency and low‐power oscillation performances. The proposed method will be simulated and compared in Matlab/Simulink environment and tested in laboratory conditions on the designed solar charge controller.