Abstract:Cooperative control consensus is one of the most actively studied topics within the realm of multi-agent systems. It generally aims to drive multi-agent systems to achieve a common group objective. The core aim of this paper is to promote research in cooperative control community by presenting the latest trends in this field. A summary of theoretical results regarding consensus for agreement analysis for complex dynamic systems and time-invariant information exchange topologies is briefly described in a unified way. The application under both non-formation and formation cooperative control consensus for multi-agent system also investigated. In addition, future recommendations and some open problems are also proposed.

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In this paper, a comprehensive review of essential components of the PV (Photovoltaic) system is elaborated, and their comparative unique features are discussed. The paper describes hardware design (power converters topologies specifically) employed in PV based energy generation systems to harvest maximum power from the available energy source. In this study, thirty different Maximum Power Point Tracking (MPPT) techniques have been critically analyzed and their response with respect to partial shading condition has been discussed. It is very difficult to say which technique is best as one must consider various factors and parameters while selecting a technique such as application, convergence speed, accuracy, efficiency, system reliability, and cost and performance of available hardware. Aiming at the complexity, hardware implementation, tracking speed, steady-state accuracy, or global maximum detection of the algorithm, an MPPT algorithm based on a rule table is proposed. In addition, the MPPT of a PV system based on bio inspired techniques is considered. The bio inspired algorithms and its application in PV system are compared for the authenticity of the review, and six different MPPT techniques are implemented on PV systems. A comparative analysis is made based on the results of four different cases of irradiance.

The penetration of renewable energy sources into the conventional power systems are evolving day by day. Therefore, in this paper, a photovoltaic (PV) connected thermal system is discussed and analyzed by keeping PV to operate at maximum power point (MPP). The main problem in the interconnection of these systems is load frequency fluctuations due to different load changing conditions. The model predictive controller (MPC) has the ability to predict the target value at real-time with fast convergence. Therefore, MPC is proposed to negate this problem by giving minimum oscillation. The comparison analysis is carried out with other conventional controllers, including genetic algorithm-based PI, firefly algorithm-based PI and PI controller. Simulation results clearly exhibit the outclass performance of MPC over all other controllers.

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Summary This paper introduces a novel genetic optimize multi‐control adaptive fractional order PID (AFOPID) for Photovoltaic (PV) and Wind connected grid system. The proposed AFOPID controller is optimized by a genetic algorithm (GA) to initialize the controller parameters. The renewable energy sources are mathematically modeled using multi control approach (MCA). In the proposed work, the MCA involves the maximum power point tracking (MPPT), dc link voltage, and current control and quadrature axis modeling. Furthermore, the current control functionalities are performed through an adaptive approach of AFOPID, where the controlling parameters are updated by measured error at every instant. The idea behind the research is to improve the tracking efficiency by introducing better control in order to gain maximum power from the source with minimized total harmonic distortion (THD). The proposed control scheme is tested using computer‐aided experimentation by varying the output of renewable energy sources, inverter uncertainty, and grid voltage variations. The results are benchmarked against the conventional fuzzy logic controllers, fractional‐order PID, and PI controllers. Moreover, to evaluate the effectiveness of the proposed controller, the MCA‐AFOPID is compared with ant colony optimization (ACO) and particle swarm optimization (PSO) optimized FOPID controller respectively. The proposed controller outperforms as compared to other controller.

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The technology has proceeded so much that the power system should be substantial and explicit to give optimal results. Ever-increasing complexities of the power system and load disparity cause frequency fluctuations leading to efficiency degradation of the power system. In order to give a suitable real power output, the system entails an extremely perceptive control technique. Consequently, an advanced control method, that is, an adaptive model predictive controller (AMPC), is suggested for load frequency control (LFC) of the series power system which comprises photovoltaic (PV), wind, and thermal power. The suggested method is considered to enhance the power system execution as well as to decrease the oscillations due to a discrepancy in the system parameters and load disturbance under a multi-area power system network. The AMPC design verifies the constant frequency by maintaining a minimum steady state error under varying load conditions. The proposed control approach pledge that the steady-state error of frequencies and interchange of tie line powers is maintained in a given tolerance constraint. The effectiveness of the proposed controller is scrutinized by conventional controllers like genetic algorithm-tuned PI (GA-PI), firefly algorithm-tuned PI (FA-PI), and model predictive controller (MPC) to show the competence of the proposed method.