Solution of optimal reactive power dispatch with FACTS devices: A survey

Yasir, Muhammad; Khan, R. D.; Raja, Muhammad Asif Zahoor; Ullah, Farman; Chaudhary, Naveed Ishtiaq; He, Yigang

doi:10.1016/j.egyr.2020.07.030

Cited by 91 publications

(44 citation statements)

References 156 publications

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“…With considering L-index (voltage stability), Extreme Learning Machine based Leontodon Algorithm (ELMLA) is substantiated in IEEE 30 bus system [49,50]. Assessment of real power loss has been done with PSO, amended PSO, enhanced PSO, widespread learning PSO, Adaptive genetic algorithm, Canonical genetic algorithm, enriched genetic algorithm, Hybrid PSO-Tabu search (PSO-TS), Ant lion (ALO), quasi-oppositional teaching learning based (QOTBO), improved stochastic fractal search optimization algorithm (ISFS), harmony search (HS), improved pseudo-gradient search particle swarm optimization and cuckoo search algorithm.…”

Section: Simulation Resultsmentioning

confidence: 99%

Real Power Loss Reduction by Extreme Learning Machine Based Leontodon Algorithm

Lenin

2021

Technol Econ Smart Grids Sustain Energy

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In this paper Extreme Learning Machine based Leontodon Algorithm (ELMLA) has been applied for solving the Real Power loss reduction problem. Key objectives of this paper are Real power loss reduction, Voltage stability enhancement and voltage deviation minimization. Leontodon Algorithm (LA) technique is an innovative swarm based optimization algorithm. In evolutionary procedure of LA, the eminence of the seeds engendered by Leontodon is rutted, and the outstanding seeds will be reserved and appraised, whereas the deprived seeds are rejected. In order to define whether a seed is tremendous or not, an enhancement of Leontodon algorithm with extreme learning machine (ELMLA) is projected in this paper. Based on fitness values the Leontodon population is segregated into outstanding and deprived Leontodons. Subsequently outstanding and deprived Leontodons are apportioned corresponding labels as +1 if outstanding or − 1 if deprived), and it has been considered as a training set, which built on extreme learning machine. Lastly, the design is applied to categorize the Leontodon seeds as excellent or deprived. Only outstanding Leontodon seeds are selected to take part in evolution procedure. Legitimacy of the Extreme Learning Machine based Leontodon Algorithm (ELMLA) is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual Real Power loss reduction is reached. Proportion of actual Real Power loss reduction is augmented.

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Section: Simulation Resultsmentioning

confidence: 99%

Real Power Loss Reduction by Extreme Learning Machine Based Leontodon Algorithm

Lenin

2021

Technol Econ Smart Grids Sustain Energy

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“…In these conditions, power plants objective function with multiple fuels, namely the Multi-Fuel Operation (MFO), which is a piecewise polynomials function (see Figure 5). The MFO can be mathematically written as (14) and (15) without and with considering VPE, respectively.…”

Section: Cost Function Considering the Valve-point Effectmentioning

confidence: 99%

“…For many years, in modern-day power systems, this sub-problem of the OPF problem has been solved to obtain a trade-off between security and economic conditions. In addition, the reactive power dispatch problem deals with the allocation of reactive power sources aiming at minimizing the power losses and keeping all of the voltage magnitudes within their acceptable limits while satisfying multiple equality and inequality constraints [14]. It is notable that quite a few objective functions are considered by researchers in this avenue of research (see Figure 3), few of which are introduced in the sequel.…”

Section: Optimal Reactive Power Dispatchmentioning

confidence: 99%

State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints

et al. 2021

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Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.

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“…Review of various optimization approaches developed for the solution and analysis of ORPD with the main goal of enhancing efficiency, performance and security of power system is presented in [3]. The author in [4] solves the ORPD problem with voltage stability enhancement, and power loss, voltage deviation minimizations using the evolutionary based Artificial Bee Colony (ABC) algorithm.…”

Section: A Literature Reviewmentioning

confidence: 99%

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

Salkuti¹

2021

IJACSA

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This paper presents the solution of multi-objective based optimal reactive power dispatch (MO-ORPD) problem by optimizing the system power losses and voltage stability enhancement index (VSEI)/L-index objectives. ORPD problem is considered as an important issue from system security and operational point of view for optimal steady-state operation of power system. Here, single-objective based ORPD problem is solved using Crow Search Algorithm (CSA) and multi-objective based ORPD problem is solved using multi-objective CSA (MO-CSA). The CSA is considered as an efficient and robust algorithm which determines the global optimal solution for solving the non-linear and discontinuous objective functions. Two standard test systems, i.e., IEEE 30 and 57 bus systems are considered to show the effectiveness, suitability and robustness of CSA and MO-CSA for solving the ORPD problem.

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Solution of optimal reactive power dispatch with FACTS devices: A survey

Cited by 91 publications

References 156 publications

Real Power Loss Reduction by Extreme Learning Machine Based Leontodon Algorithm

Real Power Loss Reduction by Extreme Learning Machine Based Leontodon Algorithm

State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

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