Solution of non-linear optimization problems in power systems†

1973 ◽  
Vol 17 (5) ◽  
pp. 1041-1058 ◽  
Author(s):  
B. K. MUKHOPADHYAY ◽  
O. P. MALIK

The environmental degradation and increased power demand has forced modern power systems to operate at the closest stability boundaries. Thereby, the power systems operations mainly focus for the inclusion of transient stability constraints in an optimal power flow (OPF) problem. Algebraic and differential equations are including in non-linear optimization problems formed by the transient stability constrained based OPF problem (TSCOPF). Notably, for a small to large power systems solving these non-linear optimization problems is a complex task. In order to achieve the increased power carrying capacity by a power line, the Flexible AC transmission systems (FACTS) devices provides the best supported means a lot. As a result, even under a network contingency condition, the security of the power system is also highly improved with FACTS devices. The FACTS technology has the potential in controlling the routing of the line power flows and the capability of interconnecting networks making the possibility of trading energy between distant agents. This paper presents a new evolutionary algorithm for solving TSCOPF problems with a FACTS device namely adaptive unified differential evolution (AuDE). The large non-convex and nonlinear problems are solved for achieving global optimal solutions using a new evolutionary algorithm called AuDE. Numerical tests on the IEEE 30-bus 6-generator, and IEEE New England 10-generator, 39-bus system have shown the robustness and effectiveness of the proposed AuDE approach for solving TSCOPF in the presence of a FACTS device such as the SSSC device. Due to the page limitation only 30-bus results are presented.


Aerospace ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 27 ◽  
Author(s):  
Manuel Pusch ◽  
Daniel Ossmann ◽  
Tamás Luspay

The model-based flight control system design for a highly flexible flutter demonstrator, developed in the European FLEXOP project, is presented. The flight control system includes a baseline controller to operate the aircraft fully autonomously and a flutter suppression controller to stabilize the unstable aeroelastic modes and extend the aircraft’s operational range. The baseline control system features a classical cascade flight control structure with scheduled control loops to augment the lateral and longitudinal axis of the aircraft. The flutter suppression controller uses an advanced blending technique to blend the flutter relevant sensor and actuator signals. These blends decouple the unstable modes and individually control them by scheduled single loop controllers. For the tuning of the free parameters in the defined controller structures, a model-based approach solving multi-objective, non-linear optimization problems is used. The developed control system, including baseline and flutter control algorithms, is verified in an extensive simulation campaign using a high fidelity simulator. The simulator is embedded in MATLAB and a features non-linear model of the aircraft dynamics itself and detailed sensor and actuator descriptions.


2014 ◽  
Vol 239 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Riccardo Rovatti ◽  
Claudia D’Ambrosio ◽  
Andrea Lodi ◽  
Silvano Martello

Author(s):  
S. Talatahari ◽  
B. Talatahari ◽  
M. Tolouei

Aims: Different chaotic APSO-based algorithms are developed to deal with high non-linear optimization problems. Then, considering the difficulty of the problem, an adaptation of these algorithms is presented to enhance the algorithm. Background: : Particle swarm optimization (PSO) is a population-based stochastic optimization technique suitable for global optimization with no need for direct evaluation of gradients. The method mimics the social behavior of flocks of birds and swarms of insects and satisfies the five axioms of swarm intelligence, namely proximity, quality, diverse response, stability, and adaptability. There are some advantages to using the PSO consisting of easy implementation and a smaller number of parameters to be adjusted; however, it is known that the original PSO had difficulties in controlling the balance between exploration and exploitation. In order to improve this character of the PSO, recently, an improved PSO algorithm, called the accelerated PSO (APSO), was proposed, and preliminary studies show that the APSO can perform superiorly. Objective: This paper presents several chaos-enhanced accelerated particle swarm optimization methods for high non-linear optimization problems. Method: Some modifications to the APSO-based algorithms are performed to enhance their performance. Then, the algorithms are employed to find the optimal parameters of the various types of hysteretic Bouc-Wen models. The problems are solved by the standard PSO, APSO, different CAPSO, and adaptive CAPSO, and the results provide the most useful method. Result: Seven different chaotic maps have been investigated to tune the main parameter of the APSO. The main advantage of the CAPSO is that there is a fewer number of parameters compared with other PSO variants. In CAPSO, there is only one parameter to be tuned using chaos theory. Conclusion: To adapt the new algorithm for susceptible parameter identification algorithm, two series of Bouc-Wen model parameters containing standard and modified Bouc-Wen models are used. Performances are assessed on the basis of the best fitness values and the statistical results of the new approaches from 20 runs with different seeds. Simulation results show that the CAPSO method with Gauss/mouse, Liebovitch, Tent, and Sinusoidal maps performs satisfactorily. Other: The sub-optimization mechanism is added to these methods to enhance the performance of the algorithm.


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