Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models

This paper presents results obtained from monitoring a 1.72 kWp photovoltaic system installed on a flat roof of a 12 m high building in Dublin, Ireland (latitude 53.4°N and longitude 6.3°E). The system was monitored between November 2008 and October 2009 and all the electricity generated was fed into the low voltage supply to the building. Monthly average daily and annual performance parameters of the PV system evaluated include: final yield, reference yield, array yield, system losses, array capture losses, cell temperature losses, PV module efficiency, system efficiency, inverter efficiency, performance ratio and capacity factor. The maximum solar radiation, ambient temperature and PV module temperature recorded were 1241 W/m2 in March, 29.5 °C and 46.9 °C in June respectively. The annual total energy generated was 885.1 kW h/kWp while the annual average daily final yield, reference yield and array yield were 2.41 kW h/kWp/day, 2.85 kW h/kWp/day and 2.62 kW h/kWp/day respectively. The annual average daily PV module efficiency, system efficiency and inverter efficiency were 14.9%, 12.6% and 89.2% respectively while the annual average daily performance ratio and capacity factor were 81.5% and 10.1% respectively. The annual average daily system losses, capture losses and cell temperature losses were 0.23 h/day, 0.22 h/day and 0.00 h/day respectively. Comparison of this system with other systems in different locations showed that the system had the highest annual average daily PV module efficiency, system efficiency and performance ratio of 14.9%, 12.6% and 81.5% respectively. The PV system’s annual average daily final yield of 2.4 kW h/kWp/day is higher than those reported in Germany, Poland and Northern Ireland. It is comparable to results from some parts of Spain but it is lower than the reported yields in most parts of Italy and Spain. Despite low insolation levels, high average wind speeds and low ambient temperature improve Ireland’s suitability.

Measured Performance of a 1.72 kW Rooftop Grid Connected Photovoltaic System in Ireland

The coexistence of ac and dc subgrids in a hybrid microgrid is likely given that modern distributed sources can either be ac or dc. Linking these subgrids is a power converter, whose topology should preferably be not too unconventional. This is to avoid unnecessary compromises to reliability, simplicity, and industry relevance of the converter. The desired operating features of the hybrid microgrid can then be added through this interlinking converter. To demonstrate, an appropriate control scheme is now developed for controlling the interlinking converter. The objective is to keep the hybrid microgrid in autonomous operation with active power proportionally shared among its distributed sources. Power sharing here should depend only on the source ratings and not their placements within the hybrid microgrid. The proposed scheme can also be extended to include energy storage within the interlinking converter, as already proven in simulation and experiment. These findings have not been previously discussed in the literature, where existing schemes are mostly for an ac or a dc microgrid, but not both in coexistence.

Autonomous Control of Interlinking Converters with Energy Storages in Hybrid AC-DC Microgrids

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

The frequency instability issue in an interconnected power system due to small disturbances can be overcome by the load frequency control (LFC) scheme using a suitable secondary controller. So, this article introduces a grasshopper optimization algorithm (GOA) based fuzzy PD-PI cascade controller for the LFC study considering a three area interconnected system. Initially, each area of the interconnected power system comprising of a thermal generating unit and further to show the impact of renewable energy sources (RES), a combination of wind generating unit and the solar-thermal generating unit is incorporated along with thermal generating unit in area-1. The study begins with the performance verification of the GOA technique over other techniques. Then to get sure about the selection of objective function, the performance of the GOA-PI controller has been verified by considering two mostly used objective functions i.e. integral time multiplied absolute error and integral square error. Lastly, the performances of the proposed controller have been verified over other controllers for the system with/without incorporating RES.

Grasshopper Optimization Algorithm based Fuzzy PD-PI Cascade Controller for LFC of Interconnected Power System Coordinate with Renewable sources

https://journals.tubitak.gov.tr/elektrik/issues/elk-19-27-4/elk-27-4-47-1809-131.pdf

Automatic generation control analysis of power system with nonlinearities andelectric vehicle aggregators with time-varying delay implementing a novel controlstrategy

This paper presents automatic generation control (AGC) of an interconnected power system having two-area thermal reheat system. The control areas are provided with single reheat turbine with generation rate constraints (GRC) of 5%/min. In this article, proportional–integral–derivative (PID) and fractional proportional–integral–derivative (FOPID) control structure are employed in automatic generation control (AGC) loop. The parameters of the control structure are tuned with a metaheuristic optimization technique, i.e., spotted hyena optimization technique (SHO). Performance of classical controller PID is investigated and compared with FOPID controller. The results reveled in the comparison FOPID control structure, gives better response than classical PID controller. For the superiority of the given optimization technique, the results are compared with PSO technique. For sensitivity analysis, the system dynamic performances are studied with large variation of step load change in both the area.

Comparative Performance Investigation of Fractional Order and Conventional PID Controller Implemented for Frequency Stability

The prime aim of this paper is to address automatic generation control of a bulky, and intricate power system containing boiler dynamics, generation rate constraints, and governor dead band non-linearities. Along this, the instability caused by inertia less photovoltaic (PV) source is brought into the discourse. To bring the deviations of frequency, and tie-line power inside a particular limit, a reliable, proficient, and expert controller is essential. To have this, higher degrees-of-freedom (DOF) structure is conglomerated with proportional-integral-derivative (PID) controller coordinated by fractional-order (FO) operator to develop 2/3DOF-FOPID controller. Further, these controllers are upgraded to 2/3 DOF-FOPIDN controller by adjoining derivative filter (N). The PID, 2DOF-FOPIDN, and 3DOF-FOPIDN controllers are designed by SOS algorithm. The performance shows that the higher DOF based controllers outweigh the 1DOF-PID controller, and simultaneously, the 3DOF-FOPIDN controller works fabulously over 2DOF-FOPIDN controller. Close by this assessments, the potential, and reliability of 3DOF-FOPIDN controller has been inspected by injecting variable load/PV generation.

Automatic generation control of a renewable energy integrated power system in the presence of fractional order 3DOF controller

The microgrid has an ability to electrify most of isolated and remote areas significantly. Microgrids are commonly structured with the integration of various renewable energy based distributed generating (DG) sources like wind plant, solar plant, fuel cell along with few conventional micro sources such as diesel engine generator, micro-turbine etc. Most of DG systems experience low inertia and large dynamics which could affect the system frequency seriously. The uncertainties like wind fluctuation, solar energy variation and load dynamics degrades the system performance especially frequency of the mcrogrid seriously. A control action has to be highly required to maintain nominal frequency under above uncertainties. The present research paper proposes a robust fractional order multi stage controller to make necessary control over system frequency under wind and solar power uncertainties. A novel grasshopper algorithm has been employed to design optimally to the above projectrd controller. Finally, the suggested grasshopper designed fractional order multi stage controller is found to be more useful in observe to the frequency control of micrgrd model.

Binod Kumar Sahu

Papers

Grasshopper Optimization Algorithm based Fuzzy PD-PI Cascade Controller for LFC of Interconnected Power System Coordinate with Renewable sources

Automatic generation control analysis of power system with nonlinearities andelectric vehicle aggregators with time-varying delay implementing a novel controlstrategy

Comparative Performance Investigation of Fractional Order and Conventional PID Controller Implemented for Frequency Stability

Automatic generation control of a renewable energy integrated power system in the presence of fractional order 3DOF controller

A grasshopper optimized FO- multistage controller for frequency control of an AC microgrid