Search

The necessity of power is growing in response to today's growing population. In order to reduce both real and reactive power losses, three devices have been combined in this research. The distributed generations (DGs) and static var compensator (SVC) with static synchronous compensator (STATCOM) play important role for reduction of power losses (both real and reactive power) and improvement of voltage profile. The research outcome presented here outlines that presents genetic algorithm (GA) may be utilised to improve power system performance and voltage profile (VP) of distribution networks by integrating DG, SVC and STATCOM planning properly. On a 37-bus distribution network, the proposed methodology has been applied. The results of this study are helpful to those focusing on the functionality of viable and upcoming electrical grids, as well as those working in the disciplines of DG, SVC and STATCOM integration for improved system performance. [ABSTRACT FROM AUTHOR]

• Recursive least squares and generalized regression neural network are used to build a real-time dynamic system model for adaptive control. • A static var compensator additional dual-channel damping controller structure is proposed for the dominant mode of low-frequency oscillation. • Static var compensator additional damping controller parameters tuning problem converted Markov decision process and a soft actor-critic algorithm is also introduced to solve the Markov decision process. As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains. [ABSTRACT FROM AUTHOR]

In this research, a complex engineering failure analysis is done for a static VAR compensator (SVC), it was installed in Perú 220 kV system. The research introduce the selected results of the simulations and field tests completed during the fault, mitigation and corrective activities performed at the thyristor valve, which is installed in the thyristor switched capacitor (TSC) and thyristor controlled reactor (TCR) of the SVC. This paper proposes the most feasible solution to overcome the failure of the TSC based on snubber resistor in the SVC. In order to investigate the causes of this failure, various measurements were carried out on the TSC. It was concluded that a snubber resistor design needs two special test for determinate a problem or abnormal condition; a special test should be done in the factory acceptance test (FAT), site acceptance test (SAT) and to execute a maintenance diagnosis, to prevent a failure in the thyristor valve from TSC. This failure has happened in the standby system, the susceptance reference is forced to follow the susceptance reference received from the active system. There is however, a delay in the transfer of data from the active to the standby system. During transient conditions this may cause a difference in thyristor firing angles between the systems. The simulated current in the standby system does not agree with the measured current, which is controlled by the active system, an advanced diagnosis and design review is developed for to avoid a sudden failure. The feasibility of these solutions was verified through the computer simulation with PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

This paper presents a novel multi-objective model of active distribution network planning based on stochastic programming and uncertain random network (URN) theory. The planning model is proposed to find the final scheme with optimal alternative, location, size and operational strategy for the candidate distribution lines, transformer substations (TSs), distribution generations (DGs), static var compensators (SVCs) and on-load tap changers (OLTCs). Firstly, a scenario-based approach is developed to analyse the uncertainties in network system, such as the demand and intermittency of renewable sources. Since the impact of multiple uncertain factors on network cannot be ignored, a network frame is then modelled by uncertain and random weights of spanning tree (ST) instead of fixed value. In order to achieve the minimization of total cost, and further the selection of a minimum spanning tree (MST) with the uncertain and random weight, a 3-dimensional uncertain space is constructed based on the combination of the previous two targets. In addition, a second-order cone programming (SOCP) is applied to cope with the multi-objective, mixed-integer nonlinear nature of the proposed planning model. Simulation is performed on a modified Pacific Gas and Electric Company (PG&E) 69-bus distribution system, and the results demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With the integration of high shares of distributed generators (DGs), both volatile DGs and demand-side resources make it increasingly difficult to cope with the various uncertainties and put forward a higher requirement for the operational flexibility in active distribution networks (ADNs). The highly integrated distribution-level power electronic devices represented by soft open point (SOP) significantly benefit the operation of ADNs. Due to the accurate and rapid power flow control provided by SOP, various flexible resources can be coordinated in the spatial and temporal aspects, which effectively increases the DG penetration of ADNs. In this paper, the multi-dimensional characteristics of distribution system flexibility are analysed and the maximum DG hosting capacity is adopted as the index to evaluate the flexibility brought by SOP. The flexibility evaluation model is further proposed to quantify the benefits of SOP-based flexible resources, such as conventional SOP, multi-terminal SOP and SOP with energy storage. Then, the original non-convex nonlinear model is converted into an effectively solved second-order cone programming (SOCP) model using convex relaxation. To tighten the deviation of convex relaxation to the predefined accuracy, a difference-of-convex programming (DCP)-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Intermittent power generation of wind turbines and photovoltaic plants creates voltage disturbances in power distribution networks which may not be acceptable to the consumers. To control the deviations of the nodal voltages, it is necessary to use fast dynamic control of the reactive power in the distribution network. Implementation of the power electronic devices, such as Static Var Compensator (SVC), enables effective dynamic state as well as a static state of the nodal voltage control in the distribution network. This paper analyzed optimal sizing and location of SVC devices by using genetic algorithm, to improve nodal voltages profile in a distribution network with dispersed photovoltaic and wind power plants. Practical application of the developed methodology was tested on an example of a real distribution network. [ABSTRACT FROM AUTHOR]

In this paper, a new control synthesis framework is developed to solve a robust stabilization problem for a single-machine infinite-bus power system with a static var compensator (SVC). The uncertainties in the infinite bus voltage and the internal and external reactances to the generating station are considered. First, control inputs for the excitation and the SVC are obtained via immersion and invariance (I&I). Then, the controller is redesigned using a parameter update law and a filter using indirect I&I adaptive control and a two-time-scale technique. The transient and steady-state performances are enhanced by introducing class K functions. The simulation results show that the developed controller improves the system performance. [ABSTRACT FROM AUTHOR]

One of the prominent problems in wind farms is voltage flicker emission. To prevent flicker emission or mitigate the impact as best as possible, a static VAr compensator (SVC) is a great candidate both economically and technically. However, SVCs cannot completely compensate the fast-changing reactive power due to delays caused by the reactive power calculation unit and the triggering fire angle of the SVC. This paper proposes a predictive control system for SVCs, by merging an additional predictive control block into the conventional control system. It is constructed based on deep neural networks, namely adaptive one-dimensional convolutional neural network (1D-CNN). The training process is conducted based on the adaptive learning weights process to enhance the prediction accuracy and training computational complexity of the 1D-CNN. Numerical results on the actual dataset in a wind farm in Manjil, Iran, have verified the forecasting accuracy and flicker mitigation of the proposed controller. [ABSTRACT FROM AUTHOR]

The power and energy of the ASDEX Upgrade (AUG) tokamak are provided by two separate 10.5 kV, 110–85 Hz networks based on the flywheel generators EZ3–EZ4 in addition to the generator EZ2 dedicated to the toroidal field coil. The 10.5 kV networks supply the thyristor converters allowing fast control of the DC currents in the AUG poloidal field coils. Two methods for improving the load power factor in the present experimental campaign of AUG have been investigated, namely the control of the phase-to-neutral voltage in thyristor converters fitted with neutral thyristors, such as the new 145 MVA modular thyristor converter system (Group 6), and reactive power control achieved by means of static VAr compensators (SVC). The paper shows that reliable compensation up to 90 MVAr was regularly achieved and that electrical transients in SVC modules can be kept at an acceptable level. The paper will discuss the results from the reactive power reduction by SVC and neutral thyristor control and draw a comparative conclusion. [Copyright &y& Elsevier]

• Proposed intelligent neuro-genetic approaches damp out LFO in electric networks. • Standard statistical indices validate the efficacy of the proposed technique. • Results comparison confirmed the superiority of the proposed approach over others. Low-frequency oscillations should be dealt with extreme care for secure electric networks. This paper tunes the critical parameters of power system stabilizers in three different electric networks in real-time, employing the neurogenetic approach to damp out the low-frequency oscillations. The first network is a single machine infinite bus power system equipped with a power system stabilizer. Besides, the second and third networks are coordinated with second-generation flexible alternating current transmission system devices, namely a unified power flow controller and static synchronous compensator in coordination with power system stabilizers, respectively. The investigation of eigenvalue and minimum damping ratio analyses for different loading conditions proves the efficiency of the proposed approach. Additionally, the time-domain simulation comparison shows the superiority of the proposed approach over the conventional method. Besides, the satisfactory values of the statistical performance measures give confidence to the proposed approach in predicting power system stabilizer parameters and thus mitigating the low-frequency oscillations in real-time. [ABSTRACT FROM AUTHOR]

• A two-stage robust planning model for the coupled system is proposed. • The uncertainty of renewable energy, load and traffic demand are jointly considered. • The interactions between transportation and active distribution system are explored. • A two-level method is used to solve the proposed robust optimization model. Widespread utilization of electric vehicles has incurred more uncertainties and led to profound interdependencies across the power grid and transportation network. This creates an urgent demand for uncertainty modelling tools. Under this context, this paper proposes a two-stage robust optimization model for expansion planning of coupled active distribution and transportation. This model identifies the best type, location and capacity for roads, distribution lines, distributed generations, static VAR compensators, on-load tap changers, energy storage systems and charging facilities. From a system-level perspective, the steady-state pattern of the traffic flow in a transportation network is characterized by the Wardrop user equilibrium. In the active distribution system, the conic relaxation-based branch flow equations are employed, and the operating status of related devices are formulated by the linearized convex constraints. Considering the interdependency between two coupled systems, a deterministic expansion planning model is first established. Furthermore, with multiple uncertainties considered, a two-stage robust optimization model is proposed to optimize the investment and operation strategies coordinately, and thus a robust optimal solution is achieved. A two-level algorithm based on a combination of the column-and-constraint generation and outer approximation technique is developed for solving the two-stage model. The results demonstrate that the robust approach can effectively reduce the operation cost of coupled systems under the worst-case scenarios. Moreover, a comparative analysis indicates that operation costs in both systems increase linearly as the uncertainty level of traffic demand increases, while increasing that of the power load does not show an obvious change in the traffic operation cost. [ABSTRACT FROM AUTHOR]

• A new methodology for engineering failure analysis associated harmonic in Static Var Compensator. • A new proposal for improvements the International Standards IEEE 519 for harmonic studies. • A real case study has been developed for the high voltage for failure analysis. The Static Var Compensator (SVC) is an important transmission component for the stability of the power system, it has 3 main components: Thyristor- controlled reactor (TCR), Thyristor-switched capacitor (TSC) and filters. The filters are the weakness part in this power system. In this kind of equipment, the worst problem associated with a filter failure is the harmonic damage in the power system, the currently international standards doesńt have enough information about the damage in the power system with high harmonic influence caused by SVC with the filter failure. In this research the main scope is to provide a new assessment procedure for energy and industrial companies for the application of equipment on the utility system, it will illustrate the harmonic assessment levels, at the point of common coupling (PCC). A real failure analysis is made for a better comprehension of the concepts of the PCC, it is very important to know the way of harmonic current flow and other parameters as: Average maximum demand load current, the short circuit ratio (SCR), the total demand distortion (TDD) and total harmonic distortion (THD). I will provide a better understanding and recommendation for the IEEE Standard 519, the dimension should address the time variations and the statistical characteristics of the harmonic levels. The harmonic is important due to levels, control on the utility system. This engineering failure analysis of the filter applications and interface with other energy companies. [ABSTRACT FROM AUTHOR]

• A planning model of coupled transportation and active distribution system is proposed. • Scenario generation technique is extended to incorporate traffic demand. • Scenario-based user equilibrium constraint is derived and embedded into the model. • A three dimensional piecewise method is designed to relax the bivariate expressions. • The importance of considering interdependencies of the coupled system is verified. The widespread utilization of electric vehicles has inspired the emerging trend of coupled transportation and distribution system, which entails the systematic methodologies to model the new planning problems. This paper proposes a scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system. With the aim of minimizing the investment and operation costs, this model determines the best alternatives, locations and sizes for candidate assets, including traffic roads, distribution lines, distribution generators, capacitor banks, static var compensators, voltage regulators, energy storage systems and charging facilities, as well as their operation strategies. First, a generated scenario method is extended to incorporate the uncertainty of the traffic flow demand. Based on multiple scenarios, the steady-state distribution of traffic flow is characterized by the Wardrop user equilibrium principle, and the corresponding equivalent constraints are derived and incorporated into the model. For active distribution system, we formulate the operation constraints for related infrastructures. Considering the interdependency between the two systems, an expansion planning model is proposed, which simultaneously optimizes the investment and operation strategies. Due to the nonlinear nature of the model, we have developed a three-dimensional piecewise linear approximation and applied second order cone relaxation to reformulate the model as a mixed-integer second order conic program; thus, the global optimal solution can be found in a reasonable time frame. Results on a test system reveal that the variations of both systems have some influences on each other, indicating the significance of considering the interdependencies between transportation and active distribution system. [ABSTRACT FROM AUTHOR]