Mean-Variance Mapping Optimization Research Articles

Hybrid Intrusion Detection Systems Based Mean-Variance Mapping Optimization Algorithm and Random Search

Hybrid Intrusion Detection Systems Based Mean-Variance Mapping Optimization Algorithm and Random Search

Enhancing Mean-Variance Mapping Optimization Using Opposite Gradient Method and Interior Point Method for Real Parameter Optimization Problems

The aim of optimization methods is to identify the best results in the search area. In this research, we focused on a mixture of the interior point method, opposite gradient method, and mean-variance mapping optimization, named IPOG-MVMO, where the solutions can be obtained from the gradient field of the cost function on the constraint manifold. The process was divided into three main phases. In the first phase, the interior point method was applied for local searching. Secondly, the opposite gradient method was used to generate a population of candidate solutions. The last phase involved updating the population according to the mean and variance of the solutions. In the experiments on real parameter optimization problems, three types of functions, which were unimodal, multimodal, and continuous composition functions, were considered and used to compare our proposed method with other meta-heuristics techniques. The results showed that our proposed algorithms outperformed other algorithms in terms of finding the optimal solution.

Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous variables; the former include transformer tap positions and settings of reactor banks, while the latter include voltage magnitude settings in generation buses. In this paper, the ORPD problem is modeled as a mixed integer nonlinear programming problem and solved through two different metaheuristic techniques, namely the Mean Variance Mapping Optimization and the genetic algorithm. As a novelty, the solution of the ORPD problem is implemented through a Python-DIgSILENT interface that combines the strengths of both software. Several tests were performed on the IEEE 6-, 14-, and 39-bus test systems evidencing the applicability of the proposed approach. The results were contrasted with those previously reported in the specialized literature, matching, and in some cases improving, the reported solutions with lower computational times.

Auto-Tuning parameters of motion cueing algorithms for high performance driving simulator based on Kuka Robocoaster.

Driving simulators have been utilized to test and evaluate products and services for a long time. Their complexity and price range from extremely simple low-cost simulators with a fixed base to very complex high-end and pricey six-degree-of-freedom simulators with the XY table. The recent novel technique that uses an industrial robot - KUKA Robocoaster - as an interactive motion simulator platform, allowing for a highly flexible workspace as well as significantly lower prices due to mass production of the fundamental mechanics. In the constrained workspace of driving simulators, motion cueing algorithms (MCAs) are commonly employed to merge the tilt gravity and translational acceleration components for simulating the linear acceleration in the real vehicle. However, there is a few MCAs developed for the motion platform, almost MCAs were implemented for the standard six-degree-of-freedom simulators in the Cartesian coordinate. The classical MCA in the cylindrical coordinate (ClCy) MCA was first developed for the novel motion platform to take advantage of enormous rotational motion to simulate lateral acceleration while compensating for the bothersome longitudinal acceleration (due to centrifugal acceleration appearing in the rotational motion) with a proper pitch tilted angle. The process of tuning MCAs for the novel motion platform is time-consuming due to both trial and error method and the disturbing motion cues generated by rotational motion, thus it needs the involvement of experts. Although there are several auto-tuning approaches for classical, optimal, and model-predictive control MCA based on fuzzy control theory or genetic optimization method, the methods were purely applied for Cartersian coordinate without taking the bothersome longitudinal acceleration into account. Therefore, this paper firstly presents the process of integrating MCAs in the novel motion platform utilizing rotational motion for simulating lateral acceleration. For the case, besides the ClCy algorithm, the classical algorithm developed for the standart six-degree-of-freedom simulators was a sample implementation due to its popular and familiar characteristics. Secondly, the proposal of the use of the mean-variance mapping optimization (MVMO) for auto-tuning parameters of the two algorithms for reducing both rotational false cues in roll and pitch channel, and longitudinal acceleration as well as washout effect. The simulation results prove that 1) The classical and other MCAs can be applied in the novel motion platform with the proposed motion conversion; 2) both algorithms with auto-tuned parameters have high performance in exploiting effectively the workspace of the motion platform, producing no false cues of angular velocity, conpensating the disturbed longitudinal acceleration, and pulling the motion platform to the initial position after the simulation task; 3) The auto-tuning method is so transparent that can manipulates the specific simulated quantities according to the tuning goals.

Multi-Period Optimal Reactive Power Dispatch Using a Mean-Variance Mapping Optimization Algorithm

Optimal reactive power dispatch plays a key role in the safe operation of electric power systems. It consists of the optimal management of the reactive power sources within the system, usually with the aim of reducing system power losses. This paper presents both a new model and a solution approach for the multi-period version of the optimal reactive power dispatch. The main feature of a multi-period approach lies on the incorporation of inter-temporal constraints that allow the number of switching operations in transformer taps and capacitor banks to be limited in order to preserve their lifetime and avoid maintenance cost overruns. The main contribution of the paper is the constraint handling approach which consists of a multiplication of sub-functions which act as penalization and allow simultaneous consideration of both the feasibility and optimality of a given candidate solution. The multi-period optimal reactive power dispatch is an inherently nonconvex and nonlinear problem; therefore, it was solved using the metaheuristic mean-variance mapping optimization algorithm. The IEEE 30-bus and IEEE 57-bus test systems were used to validate the model and solution approach. The results allow concluding that the proposed model guarantees an adequate reactive power management that meets the objective of minimizing power losses and keeping the transformer taps and capacitor bank movements within limits that allow guaranteeing their useful life.

Auto-Tuning Parameters of the Offline Optimal Motion Cueing Algorithm with Mean-Variance Mapping Optimization

A motion cueing algorithm (MCA) not only maintains the simulator within its physical limits but also generates such movements of the driving simulator that the necessary motion cues of drivers on the realistic vehicle are equivalently reproduced. The offline optimal MCA focuses on finding the best combination of the translational acceleration and tilt angles of the motion platform to maintain drivers’ motion perception. However, the best combination depends on the MCA’s parameters, tuned mainly by trial and error with experts in the loop. Moreover, for different amplitude input signals, the parameters are accordingly modified. This manual tuning procedure is so time-consuming that the generic optimization, named Mean-Variance mapping optimization, was proposed to search the suitable parameters for the optimal algorithm. This tuning method uses the specific cost function of constraint conditions such as workspace limits, avoiding false cues, and improving motion fidelity to achieve the best parameters for optimal MCA with the particular input signals.

Design of a Pitch Controller for a Wind Turbine Using Hybrid Mean-Variance Mapping Optimization

A variable-speed wind energy conversion system (WECS) has the advantage of extracting more power from the time-varying wind. To achieve this, the pitch angle is controlled to maintain the speed of the turbine and hence the generated power at a constant level, while reducing mechanical stress on the turbines. In this work, a proportional-integral (PI) controller is used for pitch angle control. The optimal PI control gains and are tuned by the hybrid mean-variance mapping optimization (MVMO-SH) technique, particle swarm optimization (PSO), and a genetic algorithm (GA). Different fitness evaluation criteria and optimization techniques are compared, and the performances of optimal controllers presented in the time domain. The results reveal that MVMO-SH achieves the minimum error criteria within a shorter time. The optimal controller design gives an error of less than in the region for which it is tuned. The performance of the optimal PI controller is designed for one operating condition in different cases of wind gust, random variation of wind, and disturbance from the grid side to mitigate line to ground fault. The performance of the controller is shown to be satisfactory in all the cases studied.

An investigation on winglet design with limited computational cost, using an efficient optimization method

A fast and efficient optimization method is proposed for winglet design optimization. The optimization method starts from a random population, whose variables are normalized in the range [0,1]. Population members are then divided into two expert groups: the free group and the guided group; each has specific tasks for the active search of the domain but with a single operator. The deliberate search of the expert groups and proper adjustment of diversity and intensification leads to rapid population direction towards global optimization. The global performance of the method is first evaluated in two standard problems. Comparison of the results with alternative methods shows that the proposed method is superior in terms of convergence speed and accuracy. Then, a winglet design optimization problem for the DLR-F6 wing is carried out. The objective function evaluation is done by a high-fidelity computational fluid dynamics method. A robust CAD-based method is used for winglet shape parameterization. Results show that the new method is able to outperform particle swarm optimization (PSO), genetic algorithm (GA), and Mean-Variance Mapping Optimization (MVMO). The optimal winglet achieves a drag coefficient reduction of 9.19% compared to the initial wing while keeping the lift coefficient unchanged. Then, the effect of winglet design variables on the optimization process is investigated. It is found that the best result with minimum computational cost is achieved when using only three winglet shape parameters of length, twist angle and angle of attack. Finally, the root bending moment minimization is added to the objective function as a structural consideration, and its effects on the results are studied.

Hybrid Mean Variance Mapping Optimization Algorithm for Solving Stochastic Based Dynamic Economic Dispatch Incorporating Wind Power Uncertainty

This paper presents a model for studying the dynamic economic load dispatch problem in power system including conventional thermal generators and wind turbines. The problem is solved using the hybrid variant Mean Variance Mapping Optimization technique. Due to the stochastic nature of wind speed, the Weibull distribution parameters of wind speed are obtained for different time intervals (24 hours). Consequently, the probability density function for the output wind power is achieved for each hour separately. The constraints for the thermal generators are considered including generation limits, ramp rate limits, valve-point loading and spinning reserve. The optimization problem is solved for different scenarios and total operating cost obtained shows the effect of wind power penetration as well as the overestimate and underestimate power probabilities.

Self adaptive cuckoo search: Analysis and experimentation

Abstract Cuckoo search (CS) algorithm, based on the brood parasitic behaviour of cuckoo species, since its inception, has proved its worth in various fields of research and can be considered as an efficient problem solver. Though CS is a very good algorithm, its performance degrades as the problem complexity increases. So a new version namely self-adaptive CS (SACS) is proposed to improve its performance. The algorithm employs adaptive parameters and hence no parameter tuning is required to be done. Here proportional population reduction based on the fitness of current best and previous best solution is followed. Secondly, in order to improve the exploration and exploitation tendencies, the Gaussian sampling mechanism known as bare-bones variant along with division of population and generations is added. The concept of Weibull distributed probability switching is also added to increase the balance between the exploration and exploitation processes. Further, CS as an extension of differential evolution (DE), genetic algorithm (GA), stability analysis with respect to Von Neumann’s stability criteria are also presented. For performance evaluation, the SACS algorithm is tested on CEC 2017 benchmark problems and compared with respect to self-adaptive DE (SaDE), JADE, success-history based adaptive DE (SHADE), SHADE with linear population size reduction (LSHADE), mean-variance mapping optimization (MVMO), cuckoo version 1.0 (CV1.0), cuckoo version new (CVnew) and others. Experimental results and statistical analysis show that the proposed SACS algorithm performs better than SaDE, JADE, CV1.0, CVnew and CS whereas comparable with respect to LSHADE, SHADE, and MVMO. Convergence profiles further validate the results.

MVMO-Based Identification of Key Input Variables and Design of Decision Trees for Transient Stability Assessment in Power Systems With High Penetration Levels of Wind Power

Unlike synchronous generators, wind turbines cannot directly respond to large disturbances, which may cause transient instability, due to their power electronic-based interface and maximum power control strategy. To effectively monitor the influence of wind turbines, this paper proposes an approach that combines decision trees (DTs), and a newly developed variant of the Mean-Variance Mapping Optimization (MVMO) algorithm, to simultaneously tackle the problem of selecting the key variables that properly reflect the transient stability performance of a system dominated by wind power, and designing the DTs for reliable online assessment of transient stability. The notion of key variables refers to the set of variables that are closely related to the modified power system transient stability performance as a consequence of the replacement of conventional power plants by wind generators. The selection of key variables is formulated as a non-linear optimization problem with weight factors as decision variables and is tackled by MVMO. A weight factor is assigned to each key variable candidate, and its value is considered to reflect the degree of influence of the key variable candidate on the splitting property and estimation accuracy of the DTs. The samples of the key variable candidates and the initialized weight factors are used to build the first group of DTs. Then, MVMO iteratively evolves the weight factors according to its special mapping function with minimizing DTs' estimation error. According to the final list of optimized weight factors, system operators can select a reduced set of variables with the largest weight factors as key variables, depending on the resulting accuracy of the DTs. Meanwhile, DTs built by using key variables are considered as the optimal performance trees for transient stability estimation. In this way, the selection of key variables and the development of DTs are made jointly and automatically, without the interference of the users of the DTs. Test results on the modified IEEE 9 bus system and a synthetic model of a real power system show that the proposed method can correctly identify the set of key variables related to wind turbine dynamics, as well as its ability to provide a reliable estimation of the transient stability margin.

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.

Validación e Identificación de Modelos de Centrales de Generación Empleando Registros de Perturbaciones de Unidades de Medición Fasorial, Aplicación Práctica Central Paute - Molino

La planificación y operación de los sistemas de potencia son comúnmente basadas en una serie de análisis que comprenden el uso de simulaciones dinámicas y en estado estacionario. En este sentido, modelar un sistema de potencia con un adecuado detalle es un requerimiento fundamental para las aplicaciones que se basan en una predicción precisa de la respuesta dinámica del sistema, como, por ejemplo, el diseño y evaluación de esquemas de protección sistémica (SPS), esquemas de separación de áreas, la sintonización de estabilizadores de sistemas de potencias (PSS), entre otros. Sobre esta base, resulta lógico preguntarse: si los modelos no representan, dentro de un margen de error aceptable, los fenómenos observados en el SEP, ¿cómo podemos confiar en los resultados de los estudios ejecutados con base en estos modelos? Considerando la extrema importancia de disponer de un modelo adecuadamente validado, el objetivo de este trabajo es desarrollar una herramienta computacional y estructurar una metodología para la Validación e Identificación de los modelos de Centrales Eléctricas (PPMV, por sus siglas en ingles), y de sus principales sistemas de control utilizando la funcionalidad software-in-the-loop (SIL) implementada mediante el algoritmo heurístico de optimización de mapeo media-varianza (MVMO, por sus siglas en inglés) en DIgSILENT PowerFactory, el cual permite comparar los resultados de las simulaciones con registros obtenidos de unidades de medición fasorial (PMUs, por sus siglas en ingles), de eventos o perturbaciones ocurridos en un sistema de potencia

Short-term economic dispatch of smart distribution grids considering the active role of plug-in electric vehicles

A new methodology to dispatch in an economic and controlled manner the energy requirements of multiple energy storage devices, dispersed generators and loads belonging to a smart distribution grid (SDG) is presented in this paper. Plug-in electric vehicles (PEV) are mainly managed over a 24-hour time window. To maximize the economic social welfare of both a utility and PEV working as prosumers, this short-term economic dispatch (ED) problem incorporates two optimization processes interacting as an integrated model. In the first, aggregators located in the SDG maximize the economic benefits obtained by a PEV group. Fuzzy inference systems are used to estimate the decisions made by each PEV during its participation in energy exchanges with the SDG, which influence the PEV benefits. Using these results and a Mean-Variance Mapping Optimization algorithm, the economic benefits of the utility are maximized in the second process and, the ED is then solved.

Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems.

Online Parameter Estimation of a Transient Induction Generator Model Based on the Hybrid Method

The knowledge of induction generator models and their parameters has gained great importance in recent years. Induction generators have been widely used in several applications, including renewable energy systems, because of their simple construction and easy operation. A successful parameter's estimation of induction generators strongly depends on the availability of a good initial parameter guess. When it is not available, the estimation process could take plenty of time to converge or even to diverge. This paper proposes a hybrid method that estimates parameters of induction generator transient models from disturbance measurements through a hybrid algorithm based on trajectory sensitivity and mean–variance mapping optimization. The method is robust regarding initial parameter guesses, requires no disconnection of the generator from the grid, and uses measurements commonly available in practice, such as generator terminal voltage and current. The system modeling for estimation purposes is based on a squirrel-cage induction generator, represented by a third-order model, connected to both a grid and a static load. The method was tested with actual measurements obtained from a small-sized power system designed in the laboratory. The results show correct estimates were successfully achieved and the model can represent the dynamic response of the system accurately.

An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions

Over the recent years, continuous optimization has significantly evolved to become the mature research field it is nowadays. Through this process, evolutionary algorithms had an important role, as they are able to obtain good results with limited resources. Among them, bio-inspired algorithms, which mimic cooperative and competitive behaviors observed in animals, are a very active field, with more proposals every year. This increment in the number of optimization algorithms is apparent in the many competitions held at corresponding special sessions in the last 10 years. In these competitions, several algorithms or ideas have become points of reference, and used as starting points for more advanced algorithms in following competitions. In this paper, we have obtained, for different real-parameter competitions, their benchmarks, participants, and winners (from the competitions’ website) and we review the most relevant algorithms and techniques, presenting the trajectory they have followed over the last years and how some of these works have deeply influenced the top performing algorithms of today. The aim is to be both a useful reference for researchers new to this interesting research topic and a useful guide for current researchers in the field. We have observed that there are several algorithms, like the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE), Mean-Variance Mapping Optimization (MVMO), and Multiple Offspring Sampling (MOS), which have obtained a strong influence over other algorithms. We have also suggested several techniques that are being widely adopted among the winning proposals, and that could be used for more competitive algorithms. Global optimization is a mature research field in continuous improvement, and the history of competitions provides useful information about the past that can help us to learn how to go forward in the future.

Optimal Placement and Tuning Approach for Design of Power System Stabilizers and Wide Area Damping Controllers Considering Transport Delay

Abstract In this paper, a novel optimal placement and parameter tuning approach for Power System Stabilizer (PSS) is proposed to damp both local and inter-area modes in mesh like power system. The design target is two-level PSS consisting of ΔP type local PSS and Δθ type Wide Area Damping Controller (WADC). For the PSS placement, modeshape with eigenvector sensitivity and coherency analysis are used to determine the appropriate placement of both local PSS and WADC. For the PSS parameter tuning, metaheuristics based approach via Mean Variance Mapping Optimization (MVMO) is employed in order to consider the time domain analysis. The proposed objective function designs at first the local PSS only to improve damping coefficient and damping ratio of eigenvalue. After that, it designs the WADC overlapping with already designed local PSS by time domain analysis, taking into account the transport delay in the remote signal between Phasor Measurement Unit (PMU) and PSS. The proposed method is applied in IEEE New England 39-Bus (NE 39-bus) system model. Then, modeshape and coherent grouping analysis give us understandable system aspect, the local PSS and the WADC optimized by using MVMO considering the transport delay improve both local and inter-area modes whereas a method ignoring the transport delay make system poorly damped.

Optimal Design of Power System Stabilizer Using Remote Signal Considering the Transport Delay

Abstract This paper proposes a novel optimal Power System Stabilizer (PSS) design method to damp inter-area oscillations in power systems. As the input of PSS, remote signals which can be obtained by Phasor Measurement Units (PMUs) are used since remote signals represent global information of the power system. The method improves both small signal stability and transient stability, taking into consideration the fluctuation range of transport delay in the communication link between PMUs and PSSs, which may significantly affect the oscillation damping performance. In the design method, Mean Variance Mapping Optimization (MVMO) is employed to determine the PSS parameters. The proposed method is applied in IEEJ (The Institute of Electrical Engineers of Japan) WEST 10-machine system model. Then, network modeshape is used to select the most appropriate remote signal. As a result of optimal design, the PSSs with both local and remote signals can give better damping than the local PSS alone, even when maximum transport delay occurs, whereas PSS designed ignoring transport delay makes the system unstable in some cases of such delay.

Validación del Modelo de un Regulador Automático de Voltaje basado en Optimización de Mapeo Media-Varianza y Pruebas de Campo

La operación y planificación de los sistemas de potencia son en gran medida basadas en una serie de análisis que involucran el uso de simulaciones dinámicas y en estado estacionario. En este contexto, el modelado a detalle de un sistema de potencia es un requerimiento básico para las aplicaciones que se basan en una predicción precisa de la respuesta dinámica del sistema, como el diseño de estrategias de protección y esquemas de control. La mayoría de los modelos únicamente corresponden a una representación matemática, cuyos parámetros deben ser, en primera instancia, ajustados o identificados con base en un proceso racional de validación de modelos que emplea frecuentemente datos experimentales. Este trabajo propone un método de estimación paramétrica para lograr la validación de modelos en sistemas de potencia a través de una simulación software-in-the-loop (SIL) implementada mediante la optimización de mapeo media-varianza (MVMO) en DIgSILENT PowerFactory, la cual permite comparar los resultados de las simulaciones con registros obtenidos de pruebas de campo. El método propuesto es luego utilizado para realizar la validación del regulador automático de voltaje (AVR) de Coca Codo Sinclair, la planta de generación hidroeléctrica más grande de Ecuador. Los resultados obtenidos son finalmente comparados con dos enfoques similares: i) el objeto “Model Parameter Identification” de PowerFactory y ii) la herramienta “Parameter Estimation” de Matlab-Simulink. Las comparaciones muestran las bondades de la propuesta para sobrellevar limitaciones de las otras dos metodologías, como precisión, restricciones y capacidad de simulación en SIL.