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

This article deals with automatic generation control (AGC) of a three-area power system having five diversified sources of generation like thermal unit, hydro unit, wind unit, diesel unit and a gas unit are interconnected together. Area-1 of the power system consists of a thermal, a hydro and a wind unit, area-2 has a thermal, a hydro and a diesel unit and area-3 consists of a thermal, a hydro and a gas unit. To make system more realistic different nonlinear components like governor dead band (GDB), generation rate constraint (GRC), boiler dynamics and communication delay are taken into account. A novel two-degree of freedom fractional order PID with derivative filter and fractional order PD with derivative filter (2-DOF-FOPIDN-FOPDN) cascaded control strategy is adopted to improve the dynamic performance of the power system. The results obtained with the proposed cascaded controller are compared with that of PID, FOPID and 2-DOF-PIDN-PDN cascaded controller to prove its superiority. To enumerate the gains of different controllers optimally, a recently developed bio-inspired optimisation algorithm named selfish herd optimisation (SHO) is capitalised. Further, the work is extended by taking a two-area hydro-thermal system to compare the result of the SHO tuned PID controller with that of modern hybrid firefly algorithm-pattern search (hFA-PS) technique. Transient analysis is carried out by applying a sudden load disturbance of 0.01 p.u in area-1 and the robustness of the controller is examined by varying both system parameters and applying a randomly varying load in area-1. From the investigation it is concluded that the 2-DOF-FOPIDN-FOPDN controller gives a flawless and a distinct performance.

Selfish Herd Optimisation tuned fractional order cascaded controllers for AGC Analysis

This article presents the study of frequency regulation of a solar energy based two area integrated power system equipped with multi-staged PID & conventional PID controller. To optimize the parameters of the projected multi-staged PID controllers, invasive weed optimization algorithm (IWOA) is employed. A disturbance to the extent of 0.01 p.u. is injected to the control area-1 to examine the effectiveness of the controllers in maintaining the system stability. Comparison of the results with respect to the magnitude of oscillations and settling time indicates that the proposed multi-staged PID controller outperforms the classical PID controller. The projected multi-staged PID controller optimised by the aforesaid algorithm (IWOA) helps in attaining nominal values of the tie-line power & frequency deviations during large parametric variation of the system and variation of system condition. Robustness of the proposed control strategy is established under various conditions such as (i) inclusion of a time delay of 0.7 s in each control area, (ii) wide variations of the system parameters and (iii) application of a random loading pattern in area 1.

https://www.ijrer.com/index.php/ijrer/article/download/8975/pdf

Application of Invasive Weed Optimization Algorithm to Optimally Design Multi-Staged PID Controller for LFC Analysis

This paper deals with design and performance scrutiny of automatic generation control (AGC) of a multi area interconnected power system. Here three thermal systems with equal ratings have been considered. Single input Fuzzy proportional integral-derivative (FPID) controller is used to study the transient response of the proposed system. In order to get realistic approach turbine time constant along with the governor dead band are used in all the three areas. The gains of FPID controller is optimized by using Teaching Learning Based Optimization (TLBO) technique & Particle Swarm Optimization (PSO). Step load perturbation (SLP) of 1 % is applied in area 1 to study the performance of the proposed controller. Finally it is observed that TLBO algorithm yields better dynamic performance.

Automatic generation control of multi-area thermal power system using TLBO algorithm optimized fuzzy-PID controller

This work focuses on the protection of distributed generation (DG) integrated microgrid system by using Kernel Extreme Learning Machine (KELM) based Hilbert–Huang Transform (HHT). Firstly, the current signals collected from buses are processed through Empirical Mode Decomposition (EMD) to obtain the Intrinsic Mode Functions (IMFs). Subsequently, the most significant IMF is used for the calculation of spectral energy and differential energy of both the buses. Subsequently, the most relevant features for protection aspects like differential energy levels, mean, median, entropy, and standard deviation are recorded for all fault types in a grid-connected environment with both radial and looped structures on IEC microgrid model test system. Further, the Gaussian kernel is used with 70% data points for the training of the neural network and optimization of the random matrix. The optimized values are then analyzed for validation and the efficiency quotient. The accuracy, security, and dependability values clearly illustrate the superiority of this optimized KELM architecture for the detection of a fault in a grid-connected microgrid system.

An Optimized Machine Learning-Based Time-Frequency Transform for Protection of Distribution Generation Integrated Microgrid System

This article considered a two equal-area multi-unit interconnected system comprising of thermal, hydro, and wind generators in each area for the study of automatic generation control. Initially, the performance of proposed Grasshopper optimization algorithm (GOA) based fuzzy-two degree of freedom-PID (F-2DOF-PID) type secondary controller is verified over other popular controllers like conventional PID, two degrees of freedom-PID (2DOF-PID) and fuzzy-PID (F-PID). As the integration of flexible AC transmission system (FACTS) and Energy storage system (ESS) improves the dynamic performances of an interconnected system so, the study is further extended to verify the effect and compare the performance of different FACTS devices coordinated with RFB type ESS for the same system by employing GOA based F-2DOF-PID controller. Finally, the system performance is verified in the presence and absence of FACTS and RFB with the application of random variation of load pattern.

Binod Kumar Sahu

Papers

Selfish Herd Optimisation tuned fractional order cascaded controllers for AGC Analysis

Application of Invasive Weed Optimization Algorithm to Optimally Design Multi-Staged PID Controller for LFC Analysis

Automatic generation control of multi-area thermal power system using TLBO algorithm optimized fuzzy-PID controller

An Optimized Machine Learning-Based Time-Frequency Transform for Protection of Distribution Generation Integrated Microgrid System

Comparative Analysis of FACTS Coordinated Hybrid Power System with RFB for AGC Using GOA Based F-2DOF-PID Controller