Identification of SVC Dynamics Using Wide Area Signals in a Power System

This paper presents the design of a wide area monitor (WAM) using remote area signals, such as speed deviations of generators in a power network, for identifying online the dynamics of a static var compensator (SVC). The design of the WAM is studied on the 12 bus FACTS benchmark system recently introduced. A predict-correct method is used to enhance the performance of the WAM during online operation. Simulation results are presented to show that WAM can correctly identify the dynamics of SVC in a power system for small and large disturbances. Such WAMs can be applied in the design of adaptive SVC controllers for damping interarea oscillations in power networks

Real-Time Implementation of a Dual Function Neuron Based Wide Area SVC Damping Controller

The use of wide area measurements for power system stabilization is recently given a lot of attention by researchers and the power industry to avoid cascading failures and blackouts such as the August 2003. This paper presents the design of a nonlinear external damping controller based on wide area measurements as inputs to a dual function neuron (DFN). This DFN controller is specifically designed to enhance the damping characteristics of a power system considering the nonlinearity in the system. The major advantage of the DFN controller is that it is simple in structure with less development time and hardware requirements for real-time implementation. The DFN controller is implemented on a digital signal processor and its performance is evaluated on the IEEE 12 bus FACTS benchmark power system implemented on a real time platform - real time digital simulator (RTDS). Experimental results show that the DFN controller provides better damping than a conventional linear controlle

Dual-Function Neuron-Based External Controller for a Static Var Compensator

The use of wide-area measurements for power system stabilization has recently been given a lot of attention by researchers and the power industry to avoid cascading failures and blackouts, such as the one in North America in August 2003. This paper presents the design of a nonlinear external damping controller based on wide-area measurements as inputs to a single dual-function neuron (DFN)-based controller. This DFN controller is specifically designed to enhance the damping characteristics of a power system over a wide range of operating conditions using an existing static var compensator (SVC) installation. The major advantage of the DFN controller is that it is simple in structure with less development time and hardware requirements for real-time implementation. The DFN controller presented in this paper is realized on a digital signal processor and its performance is evaluated on the 12-bus flexible ac transmission system benchmark test power system implemented on a real-time platform-the real-time digital simulator. Experimental results show that the DFN controller provides better damping than a conventional linear external controller and requires less SVC reactive power. The damping performance of the DFN controller is also illustrated using transient energy calculations

Online Training of a Generalized Neuron with Particle Swarm Optimization

Neural networks are used in a wide number of fields including signal and image processing, modeling and control and pattern recognition. Some of the most common type of neural networks is the multilayer perceptrons and the recurrent neural networks. Most of these networks consist of large number of neurons and hidden layers, which results in a longer training time. A Generalized Neuron (GN) has a compact structure and overcomes the problem of long training time. Due to its simple structure and lesser memory requirements, the GN is attractive for hardware implementations. This paper presents the online training of a GN with the Particle Swarm Optimization (PSO) algorithm. A comparative study of the GN and the MLP online trained with PSO is presented for function approximations. The GN based identification of the Static VAR Compensator (SVC) dynamics in a 12 bus FACTS benchmark power system trained online with the PSO is also presented

Optimal Design of SVC Damping Controllers with Wide Area Measurements Using Small Population Based PSO

Static Var Compensator (SVC) are employed for providing better voltage regulation and transient stability especially for increased power transfer through the transmission lines. In this paper, two SVC damping controllers with single and dual inputs respectively are designed based on wide area measurements of generator speed deviations. A Small Population based Particle Swarm Optimization algorithm (SPPSO) is applied to determine the optimal parameters of the damping controllers for small and large disturbances. Simulation results are provided to show that the effectiveness of the optimal damping controllers on the Kundur\u27\u27s two-area benchmark power system. Results show the dual input controller further improves the damping provided by the single input controller

Intelligent Integration of a Wind Farm to an Utility Power Network with Improved Voltage Stability

The increasing effect of wind energy generation will influence the dynamic behavior of power systems by interacting with conventional generation and loads. Due to the inherent characteristics of wind turbines, non-uniform power production causes variations in system voltage and frequency. Therefore, a wind farm requires high reactive power compensation. Flexible AC transmission systems (FACTS) devices such as SVCs inject reactive power into the system which helps in maintaining a better voltage profile. This paper presents the design of a linear and a nonlinear coordinating controller between a SVC and the wind farm inverter at the point of interconnection. The performances of the coordinating controllers are evaluated on the IEEE 12 bus FACTS benchmark power system where one of the generators is replaced by a wind farm supplying 300 MW. Results are presented to show that the voltage stability of the entire power system during small and large disturbances is improved

Damping controllers for power system oscillations using a Static Var Compensator

Damping of power system plays an important role in not only increasing the transmission capacity but also in stabilizing the power system, especially after critical faults. Static Var Compensator (SVC) is a shunt FACTS device which has capability of fast and continuous capacitive and inductive reactive power supply to the power system. SVCs are widely used for voltage stability but studies show that they are also effective in providing damping to the system. In this thesis, three types of SVC damping controllers are designed. The first damping controller is an optimal linear controller in which the parameters of a damping controller are optimized using two swarm inspired algorithms namely; Particle Swarm Optimization (PSO) and Small Population based Particle Swarm Optimization. The second damping controller is an indirect adaptive based neurocontroller (NC). This controller comprises of a Wide Area Monitor (WAM). The WAM and NC are realized using a neural structure called the Dual Function Neuron (DFN). A DFN requires shorter training time and can still maintain its fault tolerant capabilities for any complex problem. The components realized using DFNs are trained using PSO algorithm. The third controller design is an optimal NC based on Heuristic Dynamic Programming which is a part of Adaptive Critic Designs (ACDs). This design comprises of a WAM, a critic network and a controller. All these are realized using the DFN structure and trained using PSO. Results comparing the performance of designed controllers with the linear controller (tuned by trial and error) are presented based on PSCAD and RSCAD environment studies. Several analyses are presented for each of the controller to show the frequency and the damping of the dominant modes of the system --Abstract, page iii