Yuanzhang Sun

Decentralized nonlinear optimal excitation control

A design method to lay emphasis on a differential geometric approach for decentralized nonlinear optimal excitation control of multimachine power systems is suggested in this paper. The control law achieved is implemented via purely local measurements. Moreover, it is independent of the parameters of power networks. Simulations are performed on a six-machine power system. It has been demonstrated that the nonlinear optimal excitation control could adapt to the conditions under large disturbances. Besides, this paper has verified that the optimal control in the sense of LQR principle for the linearized system is equivalent to an optimal control in the sense of a quasi-quadratic performance index for the primitive nonlinear control system.

A practical, precise method for frequency tracking and phasor estimation

Comprehensive analysis of discrete Fourier transform (DFT) error is given in this paper, including why it is accurate when used in the case of synchronous sampling and how error rises when sampling frequency does not synchronized to signal frequency. Simple but precise expressions of phase angle error and amplitude error are given. Practical formulas to calculate the true phase angle and amplitude are presented. The formulas are very simple and precise. Based on the formula to calculate true phase angle, a new frequency tracking method is developed. The proposed method can be calculated recursively. And, with notable accuracy improvement, the calculation burden is little more than the traditional DFT method. Also, an adaptive method to suppress the effect of harmonics is presented, which adds very little calculation burden with satisfying performance. The most distinguished feature of the proposed method is that it is not only precise, but also simple. Some examples are given to demonstrate the feasibility, precision, simpleness and robustness of the proposed method.

Review on frequency control of power systems with wind power penetration

The increasing penetration of wind power may influence the frequency stability of power systems. Therefore, new control schemes are necessary for wind turbines and power systems to support the frequency control. Currently, most of the published control methods can be classified into 3 levels, i.e., wind turbine level, wind farm level and power system level. The wind turbine level control enables wind turbines, particularly the variable speed wind generators, to provide dynamic response and power reserves for the primary frequency control by implementing the inertial, droop or deloading controller. The wind farm level control distributes the central control command from the system to the local wind turbines and energy storage units for the desired generation. The power system level control coordinates wind farms with conventional power plants for the secondary control to recover the frequency to the reference value faster than for the no coordination control case. This paper presents a review on the latest studies in relation to the 3-level frequency control of power systems with wind power penetration.

Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability

The short-term wind power scenarios have a significant impact on the operation cost and power system reliability due to the stochastic generation scheduling of wind-integrated power systems. In order to obtain the scenarios containing the information of forecast error distribution and fluctuation distribution for short-term wind power, a scenario generation method is proposed. This paper characterizes forecast error via empirical distributions of a set of forecast bins and assumes that wind power fluctuations over unit interval follow t location-scale distribution. An inverse transform sampling from a multivariate normal distribution is adopted to generate a large number of wind power scenarios. The covariance matrix of the multivariate normal distribution is estimated to fit the distribution of historical wind power fluctuations. The proposed scenario generation method is applied to the actual aggregate wind power data in the whole regions of Irelands Power System. The results indicate that the variability of wind power scenarios can be adjusted by estimating the key range parameter in the exponential covariance structure of a multivariate normal distribution.

A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch

The existence of wind power forecast errors is one of the most challenging issues for wind power system operation. It is difficult to find a reasonable method for the representation of forecast errors and apply it in scheduling. In this paper, a probability distribution model named “versatile distribution” is formulated and developed along with its properties and applications. The model can well represent forecast errors for all forecast timescales and magnitudes. The incorporation of the model in economic dispatch (ED) problems can simplify the wind-induced uncertainties via a few analytical terms in the problem formulation. The ED problem with wind power could hence be solved by the classical optimization methods, such as sequential linear programming which has been widely accepted by industry for solving ED problems. Discussions are also extended on the incorporation of the proposed versatile distribution into unit commitment problems. The results show that the new distribution is more effective than other commonly used distributions (i.e., Gaussian and Beta) with more accurate representation of forecast errors and better formulation and solution of ED problems.

Optimal PMU Placement Considering Controlled Islanding of Power System

This paper proposes an optimal phasor measurement unit (PMU) placement model considering power system controlled islanding so that the power network remains observable under controlled islanding condition as well as normal operation condition. The optimization objectives of proposed model are to minimize the number of installed PMUs and to maximize the measurement redundancy. These two objectives are combined together with a weighting variable so that the optimal solution with minimum PMU number and maximum measurement redundancy would be obtained from the model. To reduce the number of required PMUs, the effect of zero-injection bus is considered and incorporated into the model. Furthermore, additional constraints for maintaining observability following single PMU failure or line loss are also derived. At last, several IEEE standard systems and the Polish 2383-bus system are employed to test the presented model. Results are presented to demonstrate the effectiveness of the proposed method.

A practical method to improve phasor and power measurement accuracy of DFT algorithm

Centralized control, which can overcome the ill interactional effects among decentralized controllers, impels a real need for fast and reliable computational algorithms to improve the accuracy of phasor and power measurements. This paper deals with discrete Fourier transform (DFT)-based measurement techniques. Based on the practical frequency tracking method proposed by the same authors in another paper, this paper develops practical compensation method to improve the phasor and power measurement accuracy of the DFT algorithm. The method proposed by this paper takes full advantage of the DFT algorithm. It simply modifies the DFT results. With notable accuracy improvement, the calculation burden of the proposed method is little more than the DFT algorithm. The method can be used for on-line purposes. Though the analog signal studied in this paper is sinusoidal signal, the method proposed by this paper has good performance in the presence of harmonics. Some examples are given to demonstrate the feasibility, simpleness and robustness of the proposed method.

Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method

Wind power ramp events (WPREs) have received increasing attention in recent years as they have the potential to impact the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual wind power data from a wind power plant in the Bonneville Power Administration (BPA) were selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

A Hybrid Conditions-Dependent Outage Model of a Transformer in Reliability Evaluation

Constant failure rate model used in the conventional reliability assessment of power systems cannot reflect the impacts of the various operating conditions such as transformer loading, ambient temperature, weather on component reliability. A hybrid conditions-dependent outage model (CDOM) of a transformer is proposed in this paper to include those impacts. The CDOM is the combination of three failure models: the aging failures due to the loss of mechanical strength of conductor insulation; the random failures considering weather conditions, and the outages caused by the direct trips of the overload protections. The component reliability using the proposed model has been tested under different operating conditions. The model is also applied in power system operational reliability assessment. The reliability indices using CDOM are compared with that using the condition-independent outage model. The reliability indices using the CDOMs of transformers can provide useful information for operators to understand possible system and component failure risk in real-time operation and to make important alleviation decisions.

A Model for Assessing the Power Variation of a Wind Farm Considering the Outages of Wind Turbines

Most of the published models cannot properly incorporate the outages of wind turbines in estimating the stochastic variation of the wind power output. In reality, wind turbines are exposed to an open and uncontrolled environment, sometimes harsh, to harvest energy from wind. The environmental condition of wind turbines is thus worse than that of the conventional generators. This results in a relatively higher outage probability of the wind turbines, which significantly affects the power output from a wind farm. This paper proposes a model for including the outage probability of wind turbines in simulating the power output of wind farms. The contribution of this paper is in introducing a model to represent the relationship between the outage probabilities of wind turbines and wind speed and then integrating this model with a frequency-domain wind power output model. A numerical simulation approach based on the Monte Carlo method is then proposed to simulate the wind power variation including the outage probability of wind turbines. A validation study based on the field-measured data from a real wind farm shows that the simulated wind power variation matches well with the actual field measurements when the probability-based outage model is included in the calculations.