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

Design and implementation of a novel FO-hPID-FPID controller for AGC of multiarea interconnected nonlinear thermal power system

Stock index price forecasting is the influential indicator for investors and financial investigators by which decision making capability to achieve maximum benefit with minimum risk can be improved. So, a robust engine with capability to administer useful information is desired to achieve the success. The forecasting effectiveness of stock market is improved in this paper by integrating a modified crow search algorithm (CSA) and extreme learning machine (ELM). The effectiveness of proposed modified CSA entitled as Particle Swarm Optimization (PSO)-based Group oriented CSA (PGCSA) to outperform other existing algorithms is observed by solving 12 benchmark problems. PGCSA algorithm is used to achieve relevant weights and biases of ELM to improve the effectiveness of conventional ELM. The impact of hybrid PGCSA ELM model to predict next day closing price of seven different stock indices is observed by using performance measures, technical indicators and hypothesis test (paired t-test). The seven stock indices are considered by incorporating data during COVID-19 outbreak. This model is tested by comparing with existing techniques proposed in published works. The simulation results provide that PGCSA ELM model can be considered as a suitable tool to predict next day closing price.

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Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm

The paper has addressed on design of robust fuzzy cascade controller for automatic generation control (AGC) of a multi-area power system under both step load and stochastic load perturbations. The AGC aims to maintain proper balance between net generation and active demand to pertain stability over system frequency and tie-line power effectively. A control action is highly essential to make stability over system frequency under any uncertainties. The paper proposes a robust fuzzy cascade controller to make necessary control action in the multi-area system for getting desired frequency and scheduled tie-line power under different load dynamics. Further, a novel binary dragonfly algorithm (BDA) is suggested to optimally design the proposed fuzzy cascade controller under different operating conditions. To show superiority of the proposed fuzzy cascade controller, a comparative study is synthesized over fuzzy PID and PID controllers under above uncertainties. Finally, it has been conferred that the proposed binary dragonfly algorithm-designed fuzzy cascade controller is found to be more effective in regard to AGC of multi-area power system.

Binary Dragonfly Algorithm-Designed Fuzzy Cascade Controller for AGC of Multi-area Power System with Nonlinearities

Demand-side management (DSM) plays a significant function in the smart distribution system to make informed decisions from both the consumer and supplier side with regards to energy consumption to redesign the load profile and to decrease the peak load demand. This study extensively reviews the demand-side management (DSM) strategies along with both demand response and energy efficiency policies. The major objective of this paper is to enumerate the relevant features responsible to strengthen the DSM effectively, particularly for residential energy demand and the limits to energy indicators. Secondly, the large untapped and hidden potential and the associated barriers to energy efficiency enhancement are focused and surveyed for formulating a better number of potential policy responses. This further explores the portfolio approach with bundled strategies to reflect on the power market through enhancing the strength of individual residential measures through complementary policies to reduce the weaknesses. This concludes at last with the findings of possible holistic measures related to various approaches and attributes findings that reinforce the DSM strategies to enhance energy management and cost-effectiveness. Apart from that the architecture, formulation of optimization problems, and various approaches are presented to help the readers to develop research in this direction to maximize the total system peak demand, overall load factor, and utility revenue with the minimized customer electric bill.

Residential Sector Demand Side Management: A Review

The intent of this paper assimilates the issues of automatic generation control (AGC) of a two-area power system entailing five numbers of renewable as well as conventional sources such as thermal unit, hydro unit, wind unit, diesel unit, and photovoltaic (PV) unit. To make the proposed model more realistic, some non-linearities like boiler dynamics, governor dead band (GDB), and generation rate limit (GRC) are inducted into their concerned sources. The frequency stability of the system is enhanced by assuaging the transient indices like undershoot, overshoot, rise time, and settling time by employing two degrees of freedom (2-DOF) based fractional order (FO) cascaded controller incorporating with derivative filter (N) named 2-DOF-FOPIDN-FOPDN controller. The proposed controller along with 2-DOF-PIDN-PDN, and PID controllers are designed by endorsing selfish herd optimization technique (SHOT). The performance of the proposed controller is corroborated by comparing the performance exhibited by 2-DOF-PIDN-PDN, and PID controllers. Besides this, the know-how of the recommended controller is evaluated and investigated against solar power variation, load perturbation, and systemâ€™s parameter deviation which confers the robustness of the controller is conceived.

https://www.ijrer.ijrer.org/index.php/ijrer/article/download/10236/pdf

Binod Kumar Sahu

Papers

Design and implementation of a novel FO-hPID-FPID controller for AGC of multiarea interconnected nonlinear thermal power system

Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm

Binary Dragonfly Algorithm-Designed Fuzzy Cascade Controller for AGC of Multi-area Power System with Nonlinearities

Residential Sector Demand Side Management: A Review

Application of Fractional Order Cascaded Controller for AGC Study in Power System Integrated With Renewable Sources