IEEE/CAA Journal of Automatica Sinica

From Parallel Plants to Smart Plants: Intelligent Control and Management for Plant Growth

Mengzhen Kang, Fei-Yue Wang

2017, 4(2): 161-166. doi: 10.1109/JAS.2017.7510487

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Abstract:
Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description (the crop model), prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed.

Guest Editorial for Special Issue on Control and Optimization in Renewable Energy Systems

Dianwei Qian, Chengdong Li, Qinmin Yang, Xiangyang Zhao, Yaobin Chen, Haibo He

2017, 4(2): 167-167. doi: 10.1109/JAS.2017.7510490

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Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

Qinglai Wei, Derong Liu, Yu Liu, Ruizhuo Song

2017, 4(2): 168-176. doi: 10.1109/JAS.2016.7510262

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Abstract:
This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming (ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method.

Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch

Zhanjun Huang, Zhanshan Wang, Huaguang Zhang

2017, 4(2): 177-185. doi: 10.1109/JAS.2017.7510496

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Abstract:
Multilevel feature moving average ratio method is proposed to realize an open-switch fault diagnosis for any switch of the microgrid inverter. The main steps of the proposed method include multilevel signal decomposition, coefficient reconstruction, absolute average ratio process and artificial neural network (ANN) classification. Specifically, multilevel signal decomposition is realized by using the means of multi resolution analysis to obtain the different frequency band coefficients of the three-phase current signal. The related coefficient reconstruction is executed to achieve signals decomposition in different levels. Furthermore, according to the obtained data, the absolute average ratio process is used to extract absolute moving average ratio of signal decomposition in different levels for the three-phase current. Finally, to intelligently classify the inverter switch fault and realize the adaptive ability, the ANN technology is applied. Compared to conventional fault diagnosis methods, the proposed method can accurately detect and locate the open-switch fault for any location of the microgrid inverter. Additionally, it need not set related threshold of algorithm and does not require normalization process, which is relatively easy to implement. The effectiveness of the proposed fault diagnosis method is demonstrated through detailed simulation results.

A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

Yufei Tang, Chao Luo, Jun Yang, Haibo He

2017, 4(2): 186-194. doi: 10.1109/JAS.2017.7510499

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The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming (CCSNLP), and then transformed into a deterministic nonlinear programming (NLP). To tackle this NLP problem, a three-stage framework consists of particle swarm optimization (PSO), sequential quadratic programming (SQP) and Monte Carlo simulation (MCS) is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach.

A Nonlinear Observer Approach of SOC Estimation Based on Hysteresis Model for Lithium-ion Battery

Yan Ma, Bingsi Li, Guangyuan Li, Jixing Zhang, Hong Chen

2017, 4(2): 195-204. doi: 10.1109/JAS.2017.7510502

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Abstract:
In this paper, a state of charge (SOC) estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability (ISS) theory has been proposed. According to the electrochemical performance of lithiumion battery, the equivalent circuit model with two RC networks is established, which includes hysteresis characteristic in inner electrochemical response process.The nonlinear relation between open circuit voltage (OCV) and SOC is obtained from a rapid test.Exponential fitting method is used to identify the parameters of the model.A novel state observer based on ISS theory is designed for lithium-ion battery SOC estimation.The designed observer is tested on AMESim and Simulink co-simulation.The simulation results show that the proposed method has a high SOC estimation accuracy with an error of about 2 percent.

Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment

Bing Yan, Haipei Fan, Peter B. Luh, Khosrow Moslehi, Xiaoming Feng, Chien Ning Yu, Mikhail A. Bragin, Yaowen Yu

2017, 4(2): 205-215. doi: 10.1109/JAS.2017.7510505

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Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind. Recently, we developed a hybrid Markovian and interval approach for the unit commitment and economic dispatch problem where power generation of conventional units is linked to local wind states to dampen the effects of wind uncertainties. Also, to reduce complexity, extreme and expected states are considered as interval modeling. Although this approach is effective, the fact that major wind farms are often located in remote locations and not accompanied by conventional units leads to conservative results. Furthermore, weights of extreme and expected states in the objective function are difficult to tune, resulting in significant differences between optimization and simulation costs. In this paper, each remote wind farm is paired with a conventional unit to dampen the effects of wind uncertainties without using expensive utility-scaled battery storage, and extra constraints are innovatively established to model pairing. Additionally, proper weights are derived through a novel quadratic fit of cost functions. The problem is solved by using a creative integration of our recent surrogate Lagrangian relaxation and branch-and-cut. Results demonstrate modeling accuracy, computational efficiency, and significant reduction of conservativeness of the previous approach.

An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry

Zhongyang Han, Jun Zhao, Wei Wang

2017, 4(2): 216-222. doi: 10.1109/JAS.2017.7510439

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Abstract:
As an essential energy resource in steel industry, oxygen is widely utilized in many production procedures. With different demands of the oxygen amount, a gap between the generation and consumption always occurs. Therefore, its related optimization and scheduling work along with the electricity cost to fill the gap has a great impact on daily production and efficient energy utilization in steel plant. Considering an oxygen system in a steel plant in China, a nonlinear programming model for oxygen system scheduling is proposed in this study, which concerns not only the practical characteristics of the energy pipeline network, but also the electricity cost acquired by a fitting regression modeling between the load of air separation units (ASU) and its corresponding electricity consumption. A set of constraints is formulated for restricting the practical adjusting capacity and filling the imbalance gap of oxygen. To solve the proposed scheduling model with electricity cost consideration, a particle swarm optimization (PSO) algorithm is then adopted. To verify the effectiveness of the proposed approach, a large number of experiments employing the real data from this plant are carried out, both for the fitting regression and the scheduling optimization phases. And the results demonstrate that such a practice-based solution successfully resolves the oxygen scheduling problem and simultaneously minimizes the electricity cost, which will be beneficial for the enterprise.

Multi-aiming Strategy Design for Quadruple Prism Shaped Central Receiver in Solar Power Tower System

Wenjun Huang, Yingmei Qi, Fuxing Yi, Dewen Li, Hao Wang

2017, 4(2): 223-230. doi: 10.1109/JAS.2017.7510511

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For solar power tower technology, the improvement of interception efficiency of heliostat field and the extension of central receiver's life time are two technical difficulties. To the receiver, higher interception efficiency means more thermal shocks and stronger stresses of high temperatures mainly contribute to the reduction of receiver's life time. To address these problems, a semi-random distribution strategy is proposed to select the best aiming point of the heliostat, and the distribution of onedimensional array arranged on the centerline of the receiver is carried out for further optimization. It is shown by simulation that through our optimization the temperature distribution on the receiver surface becomes much more uniform while maintaining acceptable interception efficiency.

The Initial Guess Estimation Newton Method for Power Flow in Distribution Systems

Qiuye Sun, Ling Liu, Dazhong Ma, Huaguang Zhang

2017, 4(2): 231-242. doi: 10.1109/JAS.2017.7510514

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With the increasing integration of distributed generations (DGs), there is a demand for DGs to play a more important role on the voltage regulation. Meanwhile, the high penetration of DGs could raise a technical problem that the distribution system may operate with bi-directional power flow, leading to the inadequacy of the traditional power flow. Considering this new scenario in distribution system power flow, the convergence theorem is proposed, which contributes to develop a novel selection method of the initial guess closed to the convergent solution. Moreover, to ensure the fast rate of power flow convergence, the theorem of the maximum iterations estimation is also proposed. Based on the two proposed theorems, an Initial Guess Estimation Newton method is proposed, considering different operational status of DGs and initial guess sensitivity simultaneously. Based on the standard node systems, Tongliao grid, and 69 system of USA, three simulation examples are provided to illustrate the effectiveness of the proposed method.

Short-circuit Analysis in Large-scale Distribution Systems With High Penetration of Distributed Generators

Luka V. Strezoski, Marija D. Prica

2017, 4(2): 243-251. doi: 10.1109/JAS.2017.7510517

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In this paper a short-circuit computation (SCC) procedure for large-scale distribution systems with high penetration of distributed generators based on contemporary technologies is proposed. The procedure is suitable for real-time calculations. Modeling of modern distributed generators differs from the modeling of traditional synchronous and induction generators. Hence, SCC procedures found on the presumption of distribution systems with only traditional generators are not suitable in nowadays systems. In the work presented in this paper, for computation of the state of the system with short-circuit, the improved backward/forward sweep (IBFS) procedure is used. Computation results show that the IBFS procedure is much more robust than previous SCC procedures, as it takes into account all distribution system elements, including modern distributed generators.

Variable Parameter Nonlinear Control for Maximum Power Point Tracking Considering Mitigation of Drive-train Load

Zaiyu Chen, Minghui Yin, Lianjun Zhou, Yaping Xia, Jiankun Liu, Yun Zou

2017, 4(2): 252-259. doi: 10.1109/JAS.2017.7510520

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Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking (MPPT) controller. Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then, simulations on FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code and experiments on the wind turbine simulator (WTS) based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load.

A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

Tim Mareda, Ludovic Gaudard, Franco Romerio

2017, 4(2): 260-272. doi: 10.1109/JAS.2017.7510523

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The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted. This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.

Output Tracking Control of a Hydrogen-air PEM Fuel Cell

Shiwen Tong, Jianjun Fang, Yinong Zhang

2017, 4(2): 273-279. doi: 10.1109/JAS.2017.7510526

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Hydrogen-air proton exchange membrane (PEM) fuel cell is a promising clean energy. However, the stack output tracking control is still a challenging problem due to the soft characteristic of the stack. Both over-and less-control will cause the stack flooding or oxygen lacking which dramatically decreases the life of stacks. Traditional control methods rely on the accurate model of the fuel cell system, which is a high-order nonlinear system, and involve a complex controller design process. This paper combines the data-based fuzzy cluster modeling technology with the sliding mode control and the integral actions. The sliding mode controller tracks the dynamic changes of the fuel cell system and the integral controller eliminates the steady-state errors. Simulation results demonstrate good performance of the proposed control method.

Fast Distributed Demand Response Algorithm in Smart Grid

Qifen Dong, Li Yu, Wenzhan Song, Junjie Yang, Yuan Wu, Jun Qi

2017, 4(2): 280-296. doi: 10.1109/JAS.2017.7510529

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Abstract:
This paper proposes a fast distributed demand response (DR) algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation (GaBP) solver. At the beginning of each time slot, each end-user/energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption/generation locally. The proposed demand response algorithm converges rapidly to a consumption/generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user/energysupplier estimates its energy consumption/generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm.

An Improved Approach to Test Diagnosability of Bounded Petri Nets

Ran Ning, Su Hongye, Wang Shouguang

2017, 4(2): 297-303. doi: 10.1109/JAS.2017.7510406

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Abstract:
For bounded Petri nets, Cabasino et al. propose a diagnosability test method that is based on the analysis of a modified basis reachability graph and a basis reachability diagnoser. However, its complexity is exponential in the number of nodes of the basis reachability diagnoser. In order to reduce the complexity of their method, this paper presents a new diagnosability test approach for bounded Petri nets. We present the concept of an extended basis reachability graph and prove that our approach is of polynomial complexity in the number of nodes of extended basis reachability graphs. An example is given to illustrate the application of the presented approach.

Design and Robust Performance Evaluation of a Fractional Order PID Controller Applied to a DC Motor

J. Viola, L. Angel, J.M. Sebastian

2017, 4(2): 304-314. doi: 10.1109/JAS.2017.7510535

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This paper proposes a methodology for the quantitative robustness evaluation of PID controllers employed in a DC motor. The robustness analysis is performed employing a 2³ factorial experimental design for a fractional order proportional integral and derivative controller (FOPID), integer order proportional integral and derivative controller (IOPID) and the Skogestad internal model control controller (SIMC). The factors assumed in experiment are the presence of random noise, external disturbances in the system input and variable load. As output variables, the experimental design employs the system step response and the controller action. Practical implementation of FOPID and IOPID controllers uses the MATLAB stateflow toolbox and a NI data acquisition system. Results of the robustness analysis show that the FOPID controller has a better performance and robust stability against the experiment factors.

The Exp-function Method for Some Time-fractional Differential Equations

Ahmet Bekir, Ozkan Guner, Adem Cevikel

2017, 4(2): 315-321. doi: 10.1109/JAS.2016.7510172

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In this article, the fractional derivatives in the sense of modified Riemann-Liouville derivative and the Exp-function method are employed for constructing the exact solutions of nonlinear time fractional partial differential equations in mathematical physics. As a result, some new exact solutions for them are successfully established. It is indicated that the solutions obtained by the Exp-function method are reliable, straightforward and effective method for strongly nonlinear fractional partial equations with modified Riemann-Liouville derivative by Jumarie's. This approach can also be applied to other nonlinear time and space fractional differential equations.

An Iterative Learning Approach to Identify Fractional Order KiBaM Model

Yang Zhao, Yan Li, Fengyu Zhou, Zhongkai Zhou, YangQuan Chen

2017, 4(2): 322-331. doi: 10.1109/JAS.2017.7510358

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This paper discusses the parameter and differentiation order identification of continuous fractional order KiBaM models in ARX (autoregressive model with exogenous inputs) and OE (output error model) forms. The least squares method is applied to the identification of nonlinear and linear parameters, in which the Grünwald-Letnikov definition and short memory principle are applied to compute the fractional order derivatives. An adaptive P-type order learning law is proposed to estimate the differentiation order iteratively and accurately. Particularly, a unique estimation result and a fast convergence speed can be arrived by using the small gain strategy, which is unidirectional and has certain advantages than some state-of-art methods. The proposed strategy can be successfully applied to the nonlinear systems with quasi-linear characteristics. The numerical simulations are shown to validate the concepts.

Pinning Synchronization Between Two General Fractional Complex Dynamical Networks With External Disturbances

Weiyuan Ma, Yujiang Wu, Changpin Li

2017, 4(2): 332-339. doi: 10.1109/JAS.2016.7510202

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In this paper, the pinning synchronization between two fractional complex dynamical networks with nonlinear coupling, time delays and external disturbances is investigated. A Lyapunov-like theorem for the fractional system with time delays is obtained. A class of novel controllers is designed for the pinning synchronization of fractional complex networks with disturbances. By using this technique, fractional calculus theory and linear matrix inequalities, all nodes of the fractional complex networks reach complete synchronization. In the above framework, the coupling-configuration matrix and the innercoupling matrix are not necessarily symmetric. All involved numerical simulations verify the effectiveness of the proposed scheme.

Variational Calculus With Conformable Fractional Derivatives

Matheus J. Lazo, DelfimF.M. Torres

2017, 4(2): 340-352. doi: 10.1109/JAS.2016.7510160

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Invariant conditions for conformable fractional problems of the calculus of variations under the presence of external forces in the dynamics are studied. Depending on the type of transformations considered, different necessary conditions of invariance are obtained. As particular cases, we prove fractional versions of Noether's symmetry theorem. Invariant conditions for fractional optimal control problems, using the Hamiltonian formalism, are also investigated. As an example of potential application in Physics, we show that with conformable derivatives it is possible to formulate an Action Principle for particles under frictional forces that is far simpler than the one obtained with classical fractional derivatives.

Fractional Envelope Analysis for Rolling Element Bearing Weak Fault Feature Extraction

Jianhong Wang, Liyan Qiao, Yongqiang Ye, YangQuan Chen

2017, 4(2): 353-360. doi: 10.1109/JAS.2016.7510166

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The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction. The effectiveness of the proposed method is verified through simulation signal and experiment data.

Local Bifurcation Analysis of a Delayed Fractional-order Dynamic Model of Dual Congestion Control Algorithms

Min Xiao, Guoping Jiang, Jinde Cao, Weixing Zheng

2017, 4(2): 361-369. doi: 10.1109/JAS.2016.7510151

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In this paper, we propose a delayed fractional-order congestion control model which is more accurate than the original integer-order model when depicting the dual congestion control algorithms. The presence of fractional orders requires the use of suitable criteria which usually make the analytical work so harder. Based on the stability theorems on delayed fractionalorder differential equations, we study the issue of the stability and bifurcations for such a model by choosing the communication delay as the bifurcation parameter. By analyzing the associated characteristic equation, some explicit conditions for the local stability of the equilibrium are given for the delayed fractionalorder model of congestion control algorithms. Moreover, the Hopf bifurcation conditions for general delayed fractional-order systems are proposed. The existence of Hopf bifurcations at the equilibrium is established. The critical values of the delay are identified, where the Hopf bifurcations occur and a family of oscillations bifurcate from the equilibrium. Same as the delay, the fractional order normally plays an important role in the dynamics of delayed fractional-order systems. It is found that the critical value of Hopf bifurcations is crucially dependent on the fractional order. Finally, numerical simulations are carried out to illustrate the main results.

Effective Self-calibration for Camera Parameters and Hand-eye Geometry Based on Two Feature Points Motions

Jia Sun, Peng Wang, Zhengke Qin, Hong Qiao

2017, 4(2): 370-380. doi: 10.1109/JAS.2017.7510556

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A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced, and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure.

The Switching Fractional Order Chaotic System and Its Application to Image Encryption

Jialin Hou, Rui Xi, Ping Liu, Tianliang Liu

2017, 4(2): 381-388. doi: 10.1109/JAS.2016.7510127

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Many studies on fractional order chaotic systems and secure communications have been carried out, however, switching fractional order chaotic system and its application to image encryption have not been explored yet. In this paper, a new switching fractional order chaotic system is proposed, containing fractional order Chen system and the other two fractional order chaotic systems. Chaotic attractors and dynamical analysis including Lyapunov exponent, bifurcation diagram, fractal dimension, dissipation, stability and symmetry are shown firstly. After that, some circuit simulations through Multisim are presented. By controlling switch k₁ and k₂, switching among the three fractional order chaotic subsystems can be realized. Finally, we apply the switching fractional order chaotic system to image encryption using exclusive or (XOR) encryption algorithm. The encryption scheme could increase randomness and improve speed of encryption.

Vol. 4, No. 2, 2017

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