Static Var Compensator allocation considering transient stability, voltage profile and losses

© 2017 IEEE. The purpose of this paper is to determine the optimal location, size and controller parameters of Static Var Compensator (SVC) to simultaneously improve static and dynamic objectives in a power system. Four goals are considered in this paper including transient stability, voltage profile, SVC investment cost and power loss reduction. Along with the SVC allocation for improving the system transient stability, an additional controller is used and adjusted to improve the SVC performance. Also, an estimated annual load profile including three load levels is utilized to accurately find the optimal location and capacity of SVC. By considering three load levels, the cost of power losses in the power system is decreased significantly. The combination of the active power loss cost and SVC investment cost is considered as a single objective to obtain an accurate and practical solution, while the improvement of transient stability and voltage profile of the system are considered as two separate objectives. The problem is therefore formulated as a multi-objective optimization problem, and Multi Objective Particle Swarm Optimization (MOPSO) algorithm is utilized to find the best solutions. The suggested technique is verified on a 10-generator 39-bus New England test system. The results of the nonlinear simulation indicate that the optimal sizing, location and controller parameters setting of SVC can improve significantly both static and dynamic performance of the system

Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

Applications of Artificial Intelligence in Power Systems

Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems. The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation algorithms, including particle swarm optimization (PSO), differential evolution, Ant colony optimization for the continuous domain, and harmony search techniques to solve the SSE. Moreover, support vector regression is combined with modified PSO with a proposed modification on the inertia weight in order to solve the SSE. Also, the correct accuracy of classification, the speed of training, and the final cost of using power equipment heavily depend on the selected input features. In this dissertation, multi-object PSO has been used to solve this problem. Furthermore, a multi-classifier voting scheme is proposed to get the final test output. The classifiers participating in the voting scheme include multi-SVM with different types of kernels and random forests with an adaptive number of trees. In short, the development and performance of different machine learning tools combined with evolutionary computation techniques have been studied to solve the online SSE. The performance of the proposed techniques is tested on several benchmark systems, namely the IEEE 9-bus, 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus power systems. The second problem is the non-convex, nonlinear, and non-differentiable economic dispatch (ED) problem. The purpose of solving the ED is to improve the cost-effectiveness of power generation. To solve ED with multi-fuel options, prohibited operating zones, valve point effect, and transmission line losses, genetic algorithm (GA) variant-based methods, such as breeder GA, fast navigating GA, twin removal GA, kite GA, and United GA are used. The IEEE systems with 6-units, 10-units, and 15-units are used to study the efficiency of the algorithms

Applications of Artificial Intelligence in Power Systems

Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems. The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation algorithms, including particle swarm optimization (PSO), differential evolution, Ant colony optimization for the continuous domain, and harmony search techniques to solve the SSE. Moreover, support vector regression is combined with modified PSO with a proposed modification on the inertia weight in order to solve the SSE. Also, the correct accuracy of classification, the speed of training, and the final cost of using power equipment heavily depend on the selected input features. In this dissertation, multi-object PSO has been used to solve this problem. Furthermore, a multi-classifier voting scheme is proposed to get the final test output. The classifiers participating in the voting scheme include multi-SVM with different types of kernels and random forests with an adaptive number of trees. In short, the development and performance of different machine learning tools combined with evolutionary computation techniques have been studied to solve the online SSE. The performance of the proposed techniques is tested on several benchmark systems, namely the IEEE 9-bus, 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus power systems. The second problem is the non-convex, nonlinear, and non-differentiable economic dispatch (ED) problem. The purpose of solving the ED is to improve the cost-effectiveness of power generation. To solve ED with multi-fuel options, prohibited operating zones, valve point effect, and transmission line losses, genetic algorithm (GA) variant-based methods, such as breeder GA, fast navigating GA, twin removal GA, kite GA, and United GA are used. The IEEE systems with 6-units, 10-units, and 15-units are used to study the efficiency of the algorithms

Optimal excitation controllers, and location and sizing of energy storage for all-electric ship power system

The Navy\u27s future all-electric ship power system is based on the integrated power system (IPS) architecture consisting of power generation, propulsion systems, hydrodynamics, and DC zonal electric distribution system (DC-ZEDS). To improve the power quality, optimal excitation systems, and optimal location and sizing of energy storage modules (ESMs) are studied. In this dissertation, clonal selection algorithm (CSA) based controller design is firstly introduced. CSA based controller design shows better exploitation ability with relatively long search time when compared to a particle swarm optimization (PSO) based design. Furthermore, \u27optimal\u27 small population PSO (SPPSO) based excitation controller is introduced. Parameter sensitivity analysis shows that the parameters of SPPSO for regeneration can be fined tuned to achieve fast optimal controller design, and thus exploiting SPPSO features for problem of particles get trapped in local minima and long search time. Furthermore, artificial immune system based concepts are used to develop adaptive and coordinated excitation controllers for generators on ship IPS. The computational approaches for excitation controller designs have been implemented on digital signal processors interfaced to an actual laboratory synchronous machine, and to multimachine electric ship power systems simulated on a real-time digital simulator. Finally, an approach to evaluate ESM location and sizing is proposed using three metrics: quality of service, survivability and cost. Multiple objective particle swarm optimization (MOPSO) is used to optimize these metrics and provide Pareto fronts for optimal ESM location and sizing --Abstract, page iv

Expert system application for reactive power compensation in isolated electric power systems

Effective electricity use can be an option which enables to achieve significant economy while generating and transmitting of electricity. One of the most important things is to improve the electricity quality through reactive power correction up to optimum values. The current article presents the solution to compensate the reactive power in the distribution networks, in GornoBadakhshan Autonomous Oblast (GBAO) with the use of the advanced technologies based on the data collection within real time. The article describes the methodology of fuzzy logic application and bio-heuristic algorithms for the suggested solution effectiveness to be determined. Fuzzy logic application to specify the node priority for compensating devices based on the linguistic matrix power loss and voltage gives the possibility to the expert to take appropriate solutions for compensating devices installation location to be determined. The appropriate (correct) determination of the compensating devices installation location in the electric power system ensures the effective regulation of the reactive power with the least economic costs. Optimization problems related to the active power loss minimization are solved as well as the cost minimization with compensating devices to ensure the values tan(φ) not exceeding 0.35 through reducing multi-objective problem to the single-objective one using linear convolution

Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

Benefit analysis of using soft DC links in medium voltage distribution networks

Soft DC Links are power electronic converters enabling the control of power flow between distribution feeders or networks. This thesis considers the use of Soft DC Links in Medium Voltage (MV) distribution networks to improve network operation while facilitating the integration of distributed generators (DGs). Soft DC Links include Soft Open Points (SOPs) and Medium Voltage Direct Current (MVDC) links. An SOP can be installed to replace mechanical switchgear in a network, providing controllable active power exchange between connected feeders, as well as reactive power compensation at each interface terminal. The deployment of an MVDC link enables power and voltage controls over a wider area, and facilitates the effective use of available capacity between adjacent networks. The benefits of using SOP and MVDC link in MV distribution networks were investigated. A multi-objective optimisation framework was proposed to quantify the operational benefits of a distribution network with an SOP. An optimisation method integrating both global and local search techniques was developed to determine the set-points of an SOP. It was found that an SOP can improve network operation along multiple criteria and facilitate the integration capacity of DGs. A Grid Transformer-based control method of an MVDC link was proposed, which requires only measurements at the grid transformers to determine the operation of an MVDC link. Control strategies considering different objectives were developed. The proposed control method is used in the ANGLE-DC project, which aims to trial the first MVDC link in Europe by converting an existing AC circuit to DC operation. It was found that an MVDC link can significantly increase the network hosting capacity for DG connections while reducing network losses compared to an AC line. An impact quantification of Soft DC Links was carried out on statistically-similar distribution networks, which refer to a set of networks with similar but different topological and electrical properties. A model was developed to determine the optimal allocation of Soft DC Links. It was found that a Soft DC Link can reduce the network annual cost under a wide range of DG penetration conditions. The statistical analysis provides distribution network planners with more robust decisions on the implementation of Soft DC Links