An interconnected power system requires specific restrictions to be maintained for frequency, tie-line power, and the terminal voltage of synchronized generators to avoid instability. Therefore, frequency stability and voltage regulation issues are covered individually and jointly in the current research work. Initially in test system 1, automatic generation control (AGC) investigations are done on two interconnected systems with thermal plants and electric vehicles in one area and distributed generation and electric vehicles in other area. The automatic voltage regulator (AVR) problem alone is chosen for investigation in test system 2. The third test system addresses the combined AGC and AVR issues. The performance of the fractional-order tilt-integral-derivative (TID) controller is compared with that of a widely used proportional integral derivative (PID) controller in all three test systems studies. The findings demonstrate better performance of the TID controller than PID in terms of providing superior dynamic metrics, such as low peak overshoots, undershoots, and settling time, as well as decreased oscillations amplitudes. Additionally, TID performs better than PID despite randomized load disturbance, system non-linearities, and time delays in AGC and the combined AGC and AVR problem. The PSO-tuned TID controller is insensitive to variation in load damping factor and time constants of the AVR system. Finally, the results are validated by an OPAL-RT 4510 real-time digital simulator.

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A Particle Swarm Optimization Technique Tuned TID Controller for Frequency and Voltage Regulation with Penetration of Electric Vehicles and Distributed Generations

Sliding mode (SM) has been selected as the controlling technique, and the state observer (SO) design is used as a component of active disturbance rejection control (ADRC) to reduce the knee position trajectory for therapeutic purposes. The suggested controller will improve the needed position performances for the Exoskeleton system when compared to the proportional-derivative controller (PD) and SMC as feed-forward in the ADRC approach, as shown theoretically and through computer simulations. Simulink tool is used in this comparison to analyze the nominal case and several disruption cases. The results of mathematical modeling and simulation studies demonstrated that SMC with a disturbance observer strategy performs better than the PD control system and SMC in feed-forward with a greater capacity to reject disturbances and significantly better than these controllers. Performance indices are used for numerical comparison to demonstrate the superiority of these controllers.

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Multiple Local Maximums (LM) and one Global Maximum (GM) can be present on the Power-Voltage (P-V) output curve during partial shading conditions of a Photovoltaic system. Particle Swarm Optimization (PSO) can consistently find the GM 100% of the time when the swarm size is large enough but the time to find the GM also increases as the swarm size increases - thus reducing the speed of tracking. In this work, we present the performance results of a modified PSO algorithm with dynamically reduced boundary (PSO-RB) for the search space of the particles. Our results indicate that the global maxima search time can be significantly improved while maintaining the accuracy by implementing the PSO-RB algorithm. A direct comparison between the original PSO algorithm and the new PSO-RB algorithm showed that PSO-RB can find the GM point 54.3% (over 2 times) faster than the conventional PSO.

Particle Swarm Optimization with Reducing Boundaries (PSO-RB) for Maximum Power Point Tracking of Partially Shaded PV Arrays

A PV maximum power tracking strategy for shaded conditions, based on an improved slime mold algorithm, is proposed in this research. To verify the superiority of the proposed algorithm, four bionomics algorithms—particle swarm optimization (PSO), tuna swarm optimization (TSO), squirrel search algorithm (SSA), and black widow spider algorithm (BWO)—were compared. The output parameter of the five control algorithms was summarized and analyzed. The adaptability of the algorithms was proven by setting different shading conditions. The simulation results demonstrated that the proposed algorithm possessed short response time, good tracking effect and fewer fluctuations. Eventually, the different algorithms were verified in the HIL+RCP physical platform. The experimental outcomes showed that the improved slime mold algorithm possessed the best tracking effect, with fewer power fluctuations.

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Research and Application of MPPT Control Strategy Based on Improved Slime Mold Algorithm in Shaded Conditions

Under partial shading conditions, photovoltaic (PV) modules in a solar array experience varying irradiance. A Global Maximum (GM) and multiple Local Maximums (LMs) can originate on the Power-Voltage (P-V) curve under such non-uniform irradiance conditions. There are many maximum power point tracking (MPPT) algorithms developed to obtain maximum power. In the discrete environment of sampling, limited samples might be available to predict the true value of peak and as the change of environment is dynamic, limited time is available to make the prediction of the peak. Machine learning is used to overcome the two problems mentioned above as the MPPT algorithms are not robust enough. Various techniques are used to classify PV curve type and predict MPP. In this paper we process an algorithm to categorize the PV curves in best way and machine learning techniques are used to predict the range within which MPP is present.

Improving Maximum Power Point Tracking Efficiency for Partially Shaded PV Arrays by Nested Zone Prediction via Machine Learning

Characteristic of a solar photovoltaic (PV) system changes due to the change in environmental conditions, such as light intensity and temperature. Output power of a PV system varies as these conditions change and suffers substantial power loss if the PV system is not operated at the maximum power point (MPP) of the power-voltage curve. This paper discusses the implementation and optimization of various MPPT algorithms using a low-cost Digilent Basys-3 FPGA. Fractional open circuit voltage (FOCV), perturb and observe (P&O), and incremental conductance (IC) algorithms were investigated. Algorithms were tested under various ambient conditions including abrupt irradiance changes using a programmable solar simulator and the system performances were evaluated. Data was recorded in a PC with the FPGA board interfaced using a custom-designed LabVIEW program. Our work will open-up the opportunity to employ FPGAs as a rugged solution for reliable PV system monitoring, control and power optimization replacing present day microcontrollers.

Alfredo S Beltran

Papers

Particle Swarm Optimization with Reducing Boundaries (PSO-RB) for Maximum Power Point Tracking of Partially Shaded PV Arrays

Photovoltaic Maximum Power Point Tracking Algorithms Implemented via Low-cost Field Programmable Gate Arrays