David Wenzhong Gao

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.

Study on AC Flashover Performance for Different Types of Porcelain and Glass Insulators With Non-Uniform Pollution

The ratio <i>T</i>/<i>B</i> of top to bottom surface salt deposit density (SDD) affects the ac pollution flashover performance of disk insulator strings. The ac pollution flashover stress was established for fifteen different combinations of SDD and T/B in a systematic study, making use of seven-unit suspension strings with six different disk profiles in this paper. Then a comparison was made of pollution performance for glass and porcelain disks with the same profile. The observed relation of ac flashover stress <i>EL</i> to SDD and <i>T</i>/<i>B</i> followed an equation of the form <i>EL</i>=<i>c</i>·SDD^-<i>b</i>·(1-<i>A</i>·log(<i>T</i>/<i>B</i>)) . The values of <i>c</i> , <i>b</i> and <i>A</i> were fitted to test results for glass and porcelain disks of identical bottom-rib profile, and to four other bottom-rib and external-rib profiles. A reduction in the ratio <i>T</i>/<i>B</i> from 1/1 to 1/15 gave a median 26% ± 8% increase in flashover strength, corresponding to the calculated increase in overall pollution layer resistance. Extrapolation of results for the seven-unit strings to UHV dimensions suggests that some reduction in leakage distance can be accepted in areas where there is frequent natural washing of the top surfaces of disk insulators.

Synchrophasor-Based Auxiliary Controller to Enhance the Voltage Stability of a Distribution System With High Renewable Energy Penetration

Wind energy is highly location-dependent. Many desirable wind resources in North America are located in rural areas without direct access to the transmission grid. By connecting megawatt-scale wind turbines to the distribution system, the cost of building transmission facilities can be avoided and wind power supplied to consumers can be greatly increased; however, integrating megawatt-scale wind turbines on distribution feeders will impact the distribution feeder stability, especially voltage stability. Distributed wind turbine generators (WTGs) have the capability to aid in grid stability if equipped with appropriate controllers, but few investigations are focusing on this. This paper investigates the potential of using real-time measurements from distribution phasor measurement units for a new WTG control algorithm to stabilize the voltage deviation of a distribution feeder. This paper proposes a novel auxiliary coordinated-control approach based on a support vector machine (SVM) predictor and a multiple-input and multiple-output model predictive control on linear time-invariant and linear time-variant systems. The voltage condition of the distribution system is predicted by the SVM classifier using synchrophasor measurement data. The controllers equipped on WTGs are triggered by the prediction results. The IEEE 13-bus distribution system with WTGs is used to validate and evaluate the proposed auxiliary control approach.

Study on the Wetting Process and Its Influencing Factors of Pollution Deposited on Different Insulators Based on Leakage Current

Pollution-induced flashover has a serious threat on the safe and reliable operation of power systems. It is known that the pollution flashover voltage is directly related to the wetting degree of the pollution layer. But the wetting performance characteristics and control method of pollution layer on insulators have not been properly specified in the existing pollution test standards. In this paper, the wetting processes of the pollution layer on different types of insulators have been studied in an artificial fog chamber. In our test studies, the leakage current on the surface of polluted insulators under a different test environment is measured and then the curves of leakage current varying with time are plotted. Furthermore, the main influencing factors of the wetting process of the pollution layer have been analyzed in this paper. Test results show that the wetting process of the pollution layer can be characterized well through the change of leakage current, and when the leakage current reaches the maximum value, the pollution layer is saturated with fog. Specifically, during the wetting process, the leakage current gradually increases until the maximum value and then decreases; the time for the pollution layer to become saturated is dependent on the salt-deposited density (SDD), the steam fog input rate, the temperature in the artificial fog chamber, and the type of insulators. It is found that with the increase of SDD and the decrease of the steam fog input rate and the temperature in the climate chamber, the necessary wetting time will increase, and the more complex the insulator structure is, the longer the wetting time will be.

A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.

Fault localization in Smart Grid using wavelet analysis and unsupervised learning

A wavelet based fault localization method in Smart Grid (SG) systems is proposed in this paper. In SG systems, voltage, current, frequency and phase measurements can be collected in real-time using phasor measurement units (PMUs). Based on the wavelet analysis of these measurements, the signal features can be extracted by computing the maximum wavelet transform coefficients (WTCs) and further processing them with a new hybrid clustering algorithm. The clustered signal features then form a fault contour map which can be used to locate faults in the SG system accurately. Both long-term and short-term faults of transmission line, transformer, generator, and load, which are major components of SG systems, are simulated in PSCAD and PowerWorld using the IEEE New England 39-bus system to verify the proposed method. The numerical results demonstrate the feasibility and effectiveness of our proposed method for accurate fault localization in SG systems.

Spatial-temporal characterization of synchrophasor measurement systems — A big data approach for smart grid system situational awareness

An approach for fully characterizing a synchrophasor measurement system is proposed in this paper, which aims to provide substantial data volume reduction while keep comprehensive information from synchrophasor measurements in time and spatial domains. Specifically, the optimal synchrophasor sensor placement (OSSP) problem with the effect of zero-injection buses (ZIB) is modeled and solved to ensure the minimum number of installed sensors and also the full observability of the power system. After the sensors are optimally placed, the matching pursuit decomposition algorithm is used to extract the time-frequency features for full description of the time-domain synchrophasor measurements. To demonstrate the effectiveness of the proposed characterization approach, power system situational awareness is investigated on Hidden Markov Model (HMM) based fault detection and identification using the spatial-temporal characteristics generated from the proposed approach. Several IEEE standard systems such as the IEEE 14 bus system, IEEE 30 bus system and IEEE 39 bus system are employed to validate and evaluate the proposed approach.

Statistical scheduling of economic dispatch and energy reserves of hybrid power systems with high renewable energy penetration

A statistical scheduling approach to economic dispatch and energy reserves is proposed in this paper. The proposed approach focuses on minimizing the overall power operating cost with considerations of renewable energy uncertainty and power system security. In such a system, it is challenging and yet an open question on the scheduling of economic dispatch together with energy reserves, due to renewable energy generation uncertainty, and spatially wide distribution of energy resources. The hybrid power system scheduling is formulated as a convex programming problem to minimize power operating cost, taking considerations of renewable energy generation, power generation-consumption balance and power system security. A genetic algorithm based approach is used for solving the minimization of the power operating cost. The IEEE 24-bus reliability test system (IEEE-RTS), which is commonly used for evaluating the price stability of power system and reliability, is used as the test bench for verifying and evaluating system performance of the proposed scheduling approach.

A condition monitoring system for wind turbine generator temperature by applying multiple linear regression model

The development and implementation of condition monitoring system become very important for wind industry with the increasing number of failures in wind turbine generators due to over temperature especially in offshore wind turbines where higher maintenance costs than onshore wind farms have to be paid due to their farthest locations. Monitoring the wind generators temperatures is significant and plays a remarkable role in an effective condition monitoring system. Moreover, they can be easily measured and recorded automatically by the Supervisory Control and Data Acquisition (SCADA) which gives more clarification about their behavior trend. An unexpected increase in component temperature may indicate overload, poor lubrication, or possibly ineffective passive or active cooling. Many techniques are used to reliably predict generators temperatures to avoid occurrence of failures in wind turbine generators. Multiple Linear Regression Model (MLRM) is a model that can be used to construct the normal operating model for the wind turbine generator temperature and then at each time step the model is used to predict the generator temperature by measuring the correlation between the observed values and the predicted values of criterion variables. Then standard errors of the estimate can be found. The standard error of the estimate indicates how close the actual observations fall to the predicted values on the regression line. In this paper, a new condition-monitoring method based on applying Multiple Linear Regression Model for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of an electrical generator temperatures based on the historical generator temperatures data. Case study built on a data collected from actual measurements demonstrates the adequacy of the proposed model.

Distributed Optimal Energy Management for Energy Internet

In this paper, a novel energy management framework for energy Internet with many energy bodies is presented, which features multicoupling of different energy forms, diversified energy roles, and peer-to-peer energy supply/demand, etc. The energy body as an integrated energy unit, which may have various functionalities and play multiple roles at the same time, is formulated for the system model development. Forecasting errors, confidence intervals, and penalty factor are also taken into account to model renewable energy resources to provide tradeoff between optimality and possibility. Furthermore, a novel distributed-consensus alternating direction method of multipliers (ADMM) algorithm, which contains a dynamic average consensus algorithm and distributed ADMM algorithm, is presented to solve the optimal energy management problem of energy Internet. The proposed algorithm can effectively handle the problems of power-heat-gas-coupling, global constraint limits, and nonlinear objective function. With this effort, not only the optimal energy market clearing price but also the optimal energy outputs/demands can be obtained through only local communication and computation. Simulation results are presented to illustrate the effectiveness of the proposed distributed algorithm.