Zengqiang Mi

Fault diagnosis for substation automation based on Petri nets and coding theory

In this paper, coding-based methodology for faults monitoring of discrete event systems is applied to electric power system. Using Petri nets and coding theory to perform the fault diagnosis is further studied. One feeder of the substation is modeled by Petri net using real time discrete events, and redundant places are introduced to form structure redundancy and facilitate fault diagnosis. Based on the previous work, the feeders all possible failures are analyzed and a new method to model these failures is given. Then parity check from coding theory is used to form an encoded Petri net, therefore faults can be detected and identified through error syndrome. Method to construct important matrix, generator matrix, is presented. The simulation is simple, fast and shows very high accuracy, as combining with error correction theory. The result of simulation shows that the scheme has good performance in real-time substation fault diagnosis.

Modeling and analysis of power system events

The events in power systems are an important dynamic behavior, and there is no good approach of modeling and analysis at all times due to the complexity of its behaviors. In this paper, a mathematic definition of the event is given after analyzing various power system events, and also gives a model framework of power system events, which is based on formal language theory. The hybrid dynamic behavior between power system discrete control actions and physical system is analyzed by using this framework, and a cascading event sequence occurred in the 8.14 blackout is used as an example to discuss the controllable of events. Then, the synthesis of supervisor of power system discrete events is also discussed

Dynamic performance improvement of wind farm with doubly fed induction generators using STATCOM

This paper studies the use of static synchronous compensator (STATCOM) for wind farms integration based on DFIG, and proposes a reactive power cooperative control strategy for the wind farm combined with STATCOM. A whole dynamic model for variable-speed constant-frequency wind energy system and the STATCOM are established in the PSCAD/EMTDC environment. On the basis of this model, the reactive power limit of DFIG wind power generation system based on the power relationships of overall system is calculated. Then optimization cooperative control strategy of the DFIG is proposed. Finally, simulation studies are carried out to examine the dynamic performance of the PCC with and without STATCOM. Results show that the wind farm with STATCOM cooperative compensate control strategy optimizes the reactive power and improves the dynamic performance of the wind farm when the system is in fault.

Hybrid Biogeography Based Optimization for Constrained Numerical and Engineering Optimization

Biogeography based optimization (BBO) is a new competitive population-based algorithm inspired by biogeography. It simulates the migration of species in nature to share information. A new hybrid BBO (HBBO) is presented in the paper for constrained optimization. By combining differential evolution (DE) mutation operator with simulated binary crosser (SBX) of genetic algorithms (GAs) reasonably, a new mutation operator is proposed to generate promising solution instead of the random mutation in basic BBO. In addition, DE mutation is still integrated to update one half of population to further lead the evolution towards the global optimum and the chaotic search is introduced to improve the diversity of population. HBBO is tested on twelve benchmark functions and four engineering optimization problems. Experimental results demonstrate that HBBO is effective and efficient for constrained optimization and in contrast with other state-of-the-art evolutionary algorithms (EAs), the performance of HBBO is better, or at least comparable in terms of the quality of the final solutions and computational cost. Furthermore, the influence of the maximum mutation rate is also investigated.

Research on short-term module temperature prediction model based on BP neural network for photovoltaic power forecasting

As one of the most related parameters of photovoltaic (PV) power generation, the temperature of PV modules and its prediction play very important role in PV power forecasting. A short-term step-wise temperature prediction model for PV module based on back propagation (BP) neural network is proposed in this paper. Firstly, the impact factors of PV module temperature are determined according to the PV module physical characteristics and the correlation coefficient. Secondly, two different prediction methods, direct and step-wise modeling methods based on BP neural network are applied to build the prediction models respectively. Thirdly, the mapping models between the module temperature and the impact factors for step-wise prediction are established under each weathers types. Finally, the deviations of two different kinds of prediction models are analyzed and discussed using actual operation data. The results indicate that, other things equal, the step-wise prediction model has better accuracy than the direct prediction model.

SVM based cloud classification model using total sky images for PV power forecasting

The accuracy of photovoltaic (PV) power forecasting decreases drastically under cloudy weather due to the rapid, violent and irregular fluctuation of solar irradiance. Therefore, to improve the accuracy of PV power forecasting, a detailed study on the influence of clouds in different movement and evolution patterns on solar irradiance is very necessary. The classification and recognition of different kinds of clouds are the basic of the study on the effect between the cloud and irradiance. A Support Vector Machine (SVM) based cloud classification model using the high temporal and spatial resolution sky images captured via the total sky imager installed in the PV plant is established in this paper. Firstly, the influence on irradiance under clouds of different shapes and distributions in a sky image is analyzed and four different classes of clouds are distinguished taking into account the meteorology standard as well as the preceding analysis. Secondly, the spectral and textural features are extracted by the statistical tonal analysis and gray level cooccurrence matrix (GLCM) of the sky image. Finally, a c-support vector classification (C-SVC) model with radial basis function (RBF) kernel function is built to classify the different clouds in the sky images. The experimental results show that the proposed SVM model can make reasonable classification and efficient identification for the various clouds in the sky images of PV plant.

Adaptive Backstepping Based MTPA Sensorless Control of PM-Assisted SynRM with Fully Uncertain Parameters

A nonlinear and robust adaptive backstepping based maximum torque per ampere speed sensorless control scheme with fully uncertain parameters is proposed for a permanent magnet-assisted synchronous reluctance motor. In the design of the controller, the relation to - -axis currents constrained by maximum torque per ampere control is firstly derived. Then, a fully adaptive backstepping control method is employed to design control scenario and the stability of the proposed control scenario is proven through a proper Lyapunov function candidate. The derived controller guarantees tracking the reference signals of change asymptotically and has good robustness against the uncertainties of motor parameters and the perturbation of load torque. Moreover, in allusion to the strong nonlinearity of permanent magnet-assisted synchronous reluctance motor, an active flux based improved reduced-order Luenberger speed observer is presented to estimate the speed. Digital simulations testify the feasibility and applicability of the presented control scheme.

Adaptive sliding mode backstepping control - based maximum torque per ampere control of permanent magnet-assisted synchronous reluctance motor via nonlinear disturbance observer

Combined with two approaches of sliding mode control and backstepping control, an adaptive sliding mode–based backstepping control scenario on the basis of nonlinear disturbance observer is proposed to complete maximum torque per ampere control for permanent magnet–assisted synchronous reluctance motor. The constraint relation of permanent magnet–assisted synchronous reluctance motor under maximum torque per ampere control is built, and the design of the controller is elaborated in detail. The uncertainties of modeling errors considering unmatched items are estimated through a presented nonlinear disturbance observer. The adaptive law reflecting the modeling error of the system is constructed. Globally asymptotic stability and convergence of the tracking error for the system are validated through Lyapunov stability criterion. Simulation and experimental results illustrate that external disturbances and uncertainties are observed correctly by nonlinear disturbance observer; the close-loop system controlled by the proposed controller can track the references rapidly and precisely, and the designed controller has a good robust ability.

A fast multi-step prediction and rolling optimization excitation control method for multi-machine power system

Original scientific paper A fast excitation predictive control method for multi-machine power system is presented. The multi-step prediction technique is realized via system dynamic model. Some inequality constraints on states, inputs and outputs are considered in rolling optimization. The Gramian balanced reduction technique and the improved optimization algorithm are used in order to save the time of open-loop optimization in model predictive control. A 50machine power system is used to verify the effectiveness of this approach. Compared with simulated results under different controllers, this method can greatly reduce the calculating-time. The voltages of generator terminals are maintained within the set points. The stability of power system is improved.

A classified irradiance forecast approach for solar PV prediction based on wavelet decomposition

A classified irradiance forecast approach for solar PV prediction is proposed based on wavelet decomposition. The Daubechies wavelet is chose to decompose the irradiance series measured in the PV plant into approximate component and detailed component. The trend and variability of irradiance series are estimated respectively based on the two components. Then all the available irradiance data are labeled according to the features extracted from the approximate and detailed components. In the end, multiple forecast models are built and trained to adapt to the irradiance series of different labels. The simulation results show the effectiveness of the proposed approach.