Jinyu Wen

Wide-Area Damping Controller of FACTS Devices for Inter-Area Oscillations Considering Communication Time Delays

The usage of remote signals obtained from a wide-area measurement system (WAMS) introduces time delays to a wide-area damping controller (WADC), which would degrade system damping and even cause system instability. The time-delay margin is defined as the maximum time delay under which a closed-loop system can remain stable. In this paper, the delay margin is introduced as an additional performance index for the synthesis of classical WADCs for flexible ac transmission systems (FACTS) devices to damp inter-area oscillations. The proposed approach includes three parts: a geometric measure approach for selecting feedback remote signals, a residue method for designing phase-compensation parameters, and a Lyapunov stability criterion and linear matrix inequalities (LMI) for calculating the delay margin and determining the gain of the WADC based on a tradeoff between damping performance and delay margin. Three case studies are undertaken based on a four-machine two-area power system for demonstrating the design principle of the proposed approach, a New England ten-machine 39-bus power system and a 16-machine 68-bus power system for verifying the feasibility on larger and more complex power systems. The simulation results verify the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performance.

Adaptive Learning in Tracking Control Based on the Dual Critic Network Design

In this paper, we present a new adaptive dynamic programming approach by integrating a reference network that provides an internal goal representation to help the systems learning and optimization. Specifically, we build the reference network on top of the critic network to form a dual critic network design that contains the detailed internal goal representation to help approximate the value function. This internal goal signal, working as the reinforcement signal for the critic network in our design, is adaptively generated by the reference network and can also be adjusted automatically. In this way, we provide an alternative choice rather than crafting the reinforcement signal manually from prior knowledge. In this paper, we adopt the online action-dependent heuristic dynamic programming (ADHDP) design and provide the detailed design of the dual critic network structure. Detailed Lyapunov stability analysis for our proposed approach is presented to support the proposed structure from a theoretical point of view. Furthermore, we also develop a virtual reality platform to demonstrate the real-time simulation of our approach under different disturbance situations. The overall adaptive learning performance has been tested on two tracking control benchmarks with a tracking filter. For comparative studies, we also present the tracking performance with the typical ADHDP, and the simulation results justify the improved performance with our approach.

Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling

This paper proposed a probabilistic load flow method that can address the correlated power sources and loads. The proposed probabilistic load flow method is based on the Nataf transformation and the Latin Hypercube Sampling. The main advantage of the proposed method is that high accurate solution can be obtained with less computation. Also, it is almost unconstrained for the probability distributions of the input random variables. Considering the uncertainties of correlated wind power, solar energy and loads, the effectiveness and the accuracy of the proposed probabilistic load flow method are verified by the comparative tests in a modified IEEE 14-bus system and a modified IEEE 118-bus system.

Wide-Area Damping Controller for Power System Interarea Oscillations: A Networked Predictive Control Approach

Wide-area damping controller (WADC) requires communication networks to transmit remote signals. The usage of communication networks will introduce time delays into the control loop of the WADC. Ignoring this time delay would deteriorate the damping performance provided by the WADC or even cause the whole system instability. This paper employs networked predictive control (NPC) to design a WADC for the generator exciter to enhance the damping of interarea oscillations in a large-scale power system. The NPC incorporates with a generalized predictive control (GPC) to generate optimal control predictions, and a network delay compensator to detect and compensate both constant and random delays. Moreover, model identification is used to obtain an equivalent reduced-order model of the large-scale power system and deal with the model uncertainties and variation of operating conditions. Case studies are based on the New England 10-machine 39-bus system. Effectiveness of the proposed WADC is verified by simulation studies and compared with a conventional WADC and a GPC-based WADC without delay compensation.

Power System Stability Control for a Wind Farm Based on Adaptive Dynamic Programming

In this paper, a goal representation heuristic dynamic programming (GrHDP) based controller is developed for the doubly-fed induction generator based wind farm to improve the system transient stability under fault conditions. The proposed controller is based on adaptive dynamic programming (ADP) techniques to approximate the optimal control policy according to the interaction between the controller and the power plant. Compared to existing ADP approaches with one action network and one critic network, our GrHDP architecture introduces an additional network, i.e., the reference network, to form an internal goal/reward representation. This better mapping between the system state and the control action significantly improves the control performance. The effectiveness of the proposed approach is validated via two cases. The first case investigates a revised four-machine two-area system with high wind penetration and a static synchronous compensator. The second case is a practical size power system with wind farm in Liaoning Province in China. Detailed simulation analysis and comparative studies with traditional ADP approaches are presented to demonstrate the superior performance of our method.

Integrating wind farm to the grid using hybrid multi-terminal HVDC technology

Since wind generation is one of the most mature renewable energy technologies, it will have the greatest share of future renewable energy portfolio. Due to the special characteristics of the wind generation, it requires extensive research to explore the best choice for wind power integration. In light of the practical project experience, this paper explores the feasibility of using HVdc transmission technology, particularly multiterminal HVdc (MTDC), as one of the preferable solutions to solve the grid interconnection issue of wind generation. This paper mainly focuses on the application of the hybrid MTDC to integrate wind farms into the electric power grid. A five-terminal hybrid MTDC model system including a large capacity wind farm is set up in PSCAD/EMTDC, in which the corresponding control strategy is designed. The operation characteristic of the hybrid system is studied, and the proposed control strategy is verified through simulation under various conditions, including wind speed variation and faults on ac side and dc side.

Goal Representation Heuristic Dynamic Programming on Maze Navigation

Goal representation heuristic dynamic programming (GrHDP) is proposed in this paper to demonstrate online learning in the Markov decision process. In addition to the (external) reinforcement signal in literature, we develop an adaptively internal goal/reward representation for the agent with the proposed goal network. Specifically, we keep the actor-critic design in heuristic dynamic programming (HDP) and include a goal network to represent the internal goal signal, to further help the value function approximation. We evaluate our proposed GrHDP algorithm on two 2-D maze navigation problems, and later on one 3-D maze navigation problem. Compared to the traditional HDP approach, the learning performance of the agent is improved with our proposed GrHDP approach. In addition, we also include the learning performance with two other reinforcement learning algorithms, namely Sarsa(λ) and Q-learning, on the same benchmarks for comparison. Furthermore, in order to demonstrate the theoretical guarantee of our proposed method, we provide the characteristics analysis toward the convergence of weights in neural networks in our GrHDP approach.

Energy-Storage-Based Low-Frequency Oscillation Damping Control Using Particle Swarm Optimization and Heuristic Dynamic Programming

Low-frequency oscillation is one of the main barriers limiting power transmission between two connected power systems. Although power system stabilizers (PSSs) have been proved to be effective in damping inner-area oscillation, inter-area oscillation still remains a critical challenge in todays power systems. Since the low-frequency oscillation between two connected power systems is active power oscillation, power modulation through energy storage devices (ESDs) can be an efficient and effective way to maintain such power system stability. In this paper, we investigate the integration of a new goal representation heuristic dynamic programming (GrHDP) algorithm to adaptively control ESD to damp inter-area oscillation. A particle swarm optimization (PSO)-based power oscillation damper (POD) has also been proposed for comparison. Various simulation studies with residue-based POD controller design, the proposed PSO optimized controller design, and the GrHDP-based controller design over a four-machine-two-area benchmark power system with energy storage device have been conducted. Simulation results have demonstrated the efficiency and effectiveness of the GrHDP-based approach for inter-area oscillation damping in a wide range of system operating conditions.

A Novel SVC Allocation Method for Power System Voltage Stability Enhancement by Normal Forms of Diffeomorphism

Location of the static VAR compensator (SVC) and other types of shunt compensation devices is important for the enhancement of the voltage stability for practical power systems. With the theory of the normal forms of diffeomorphism, this paper proposes a new method to solve this problem. The proposed method makes use of the nonlinear participation factors, in which the nonlinearity of power systems can be taken into consideration. As a result, the most suitable location where the SVC should be used in power system can be determined, even for the cases in which the system is characterized with strong nonlinearity. In order to show the effectiveness of the proposed method, the New England 39-bus power system with SVC is used as an example. Calculation results show that with the SVC located at the place where the proposed method determined, the voltage stability is considerably enhanced. The steady-state voltage stability index and the time domain simulation results verify the effectiveness of the proposed method.

A Discrete Point Estimate Method for Probabilistic Load Flow Based on the Measured Data of Wind Power

Probabilistic load flow (LF) calculation is the first step to evaluate the potential impact of the integrated wind power on the power system. Although research works show that the wind speed can be modeled by a Weibull probability density function (pdf), due to the nonlinear relationship between the wind speed and the wind power as well as many other influencing factors, it is hard to fit wind power to any common pdfs. At the same time, the relationship between the input and output variables of LF calculation is nonlinear. In view of the two characteristics, the point estimate method and Gram-Charlier expansion method are combined. Based only on the sample data of the wind power, the expectation, variance, and cumulative distribution of the output random variables can be estimated with the method by 2n + 1 times of LF calculation, where n is the number of input stochastic variables, eliminating the need for the distribution of the input variables. The simulation results on the New England Test System and New York Power Pool (NETS-NYPP) system and the actual Northeast China Grid show that the proposed method provides higher precision with less computation burden. The method can also be applied to other problems with uncertainty factors whose distribution is unknown in the power system.