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  • 1.
    Andreasson, Martin
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Nazari, Mohammad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Distributed Voltage and Current Control of Multi-Terminal High-Voltage Direct Current Transmission Systems2014In: Proceedings of the 19th IFAC World Congress, 2014, IFAC Papers Online, 2014, Vol. 19, p. 11910-11916Conference paper (Refereed)
    Abstract [en]

    High-voltage direct current (HVDC) is a commonly used technology for long-distance power transmission, due to its low resistive losses and low costs. In this paper, a novel distributed controller for multi-terminal HVDC (MTDC) systems is proposed. Under certain conditions on the controller gains, it is shown to stabilize the MTDC system. The controller is shown to always keep the voltages close to the nominal voltage, while assuring that the injected power is shared fairly among the converters. The theoretical results are validated by simulations, where the affect of communication time-delays is also studied.

  • 2.
    Babazadeh, Davood
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Nazari, Mohammad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Fidai, Muhammad Hassan
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Chenine, Moustafa
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Implementation of agent-based power flow coordination in AC/DC grids using co-simulation platform2014In: 2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014, 2014, p. 188-193Conference paper (Refereed)
    Abstract [en]

    This paper presents work on the coordination of power sharing contribution of converters in an overlaid HVDC grid using a Multi-Agent System (MAS) approach. This approach is further implemented in a real-time co-simulation platform in order to study the proposed control scheme including the supporting information and communication Technology (ICT) systems. The platform consists of OPNET, a communication network simulator, connected to a real-time power system simulator through virtualized and real devices. Furthermore, the impact of different supporting system parameters such as bit-error rate has been studied using this real-time co-simulation platform.

  • 3.
    Chamorro, Harold R.
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Nazari, Mohammad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Babazadeh, Davood
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Malik, Naveed ur Rehman
    KTH, School of Electrical Engineering (EES), Electrical Energy Conversion.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Consensus Control for Induction Motors Speed Regulation2014In: 2014 16TH European Conference on Power Electronics and Applications, 2014Conference paper (Refereed)
    Abstract [en]

    Cyber Physical Energy Systems (CPES) development requires the combination of distributed intelligence to fulfill the future complex tasks and reach the increase the energy demands. Electrical Industrial Systems (EIS) are in continuous evolving integrating new technologies allowing to a better performance and increase the efficiency. This paper applies the consensus protocol for Multi-Agent Systems (MAS) to control the speed of multiple induction motors. In this paper, the behaviour of the system under different disturbances and scenarios has been simulated, thus, confirming the suitability and simplicity of this method for coordinating the control actions.

  • 4.
    Nazari, Mohammad
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Control and Planning of Multi-Terminal HVDC Transmission Systems2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    With recent advances in power electronic technology, high-voltage direct current (HVDC) transmission system has become an alternative for transmitting power, especially over long distances. Multi-terminal HVDC (MTDC) systems are proposed as HVDC systems with more than two terminals. In addition, the wind is becoming one of the most important sources of renewable energy in the world, with vast sources available in offshore areas. MTDC systems are attractive solutions for connecting offshore wind farms to AC grids.

     

    This thesis discusses three scopes of MTDC systems: primary control, secondary control, and AC-DC transmission expansion planning. 

    In the primary control part, sliding mode control and multi-agent control are proposed. The sliding mode control can control the system fast and with very small overshoot and compared to proposed methods in the literature, it is less sensitive to changes in parameters. In the proposed multi-agent control strategy, we aim to find a solution for the problems caused by lack of global signal in the control of MTDC systems.

     

    In the secondary control part, we propose a controller, based on multi-agent systems, which follows the variations of wind and minimize the DC transmission and conversion losses, while considering the price of energy in each AC system and the scheduled injected power to each AC grid. The controller operates in both centralized and distributed modes.

    In the expansion planning part, we aim to propose a methodology to determine the optimal configuration of the MTDC system. The goal is to maximize the transferred power from the wind farms to the onshore grids while minimizing the investment cost. We propose a two-stage mixed-integer second order cone program (MISOCP) for optimal expansion of both DC and AC networks. The two-stage MISOCP is solved using the parallelized Benders decomposition algorithm.

  • 5.
    Nazari, Mohammad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Control of DC voltage in Multi-Terminal HVDC Transmission (MTDC) Systems2014Licentiate thesis, monograph (Other academic)
    Abstract [en]

    With recent advances in power electronic technology, High-Voltage Direct Current (HVDC) transmission system has become an alternative for transmitting power especially over long distances. Multi-Terminal HVDC (MTDC) systems are proposed as HVDC systems with more than two terminals. These systems can be geographically wide. While in AC grids, frequency is a global variable, in MTDC systems, DC voltage can be considered as its dual. However, unlike frequency, DC voltage can not be equal across the MTDC system. Control of DC voltage in MTDC systems is one of the important challenges in MTDC systems. Since the dynamic of MTDC system is very fast, DC voltage control methods cannot rely only on remote information. Therefore, they can work based on either local information or a combination of local and remote information. In this thesis, first, the MTDC system is modeled. One of the models presented in this thesis considers only the DC grid, and effects of the AC grids are modeled with DC current sources, while in the other one, the connections of the DC grid to the AC grids are also considered. Next, the proposed methods in the literature for controlling the DC voltage are described and in addition to these methods, some control methods are proposed to control the DC voltage in MTDC system. These control methods include two groups. The first group (such as Multi-Agent Control methods) uses remote and local information, while the second group (such as Sliding Mode Control and H¥ control) uses local information.The proposed multi-agent control uses local information for immediate response, while uses remote information for a better fast response. Application of Multi-Agent Control systems leads to equal deviation of DC voltages from their reference values. Using remote information leads to better results comparing to the case only local information is used. Moreover, the proposed methods can also work in the absence of remote information. When AC grid is considered in the modeling, the MTDC system has anon-linear dynamic. Sliding Mode Control, a non-linear control method with high disturbance rejection capability, which is non-sensitive to the parameter variations, is applied to the MTDC system. It controls the DC voltage very fast and with small or without overshoot. Afterward, a static state feedback H¥ control is applied to the system which minimizes the voltage deviation after a disturbance and keeps the injected power of the terminals within the limits. Finally, some case studies are presented and the effectiveness of the proposed methods are shown. All simulations have been done in MATLAB and SIMULINK.

  • 6.
    Nazari, Mohammad
    et al.
    KTH, School of Electrical Engineering (EES), Electrical Energy Conversion.
    Baradar, Mohamadreza
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    On-line control of multi-terminal HVDC systems connected to offshore wind farms using the POF-based multi-agent approarch2015Conference paper (Other academic)
    Abstract [en]

    Multi-terminal HVDC systems are an attractive option to connect offshore wind farms to onshore grids. Although scheduling the multi-terminal HVDC system is based on forecasted wind power, the forecasted values may differ from their real time ones. This paper presents a new controller based on multi-agent system which optimally tries to follow the variations of real time wind power outputs. Since a fast optimal power flow algorithm is needed, a convexified AC-OPF model which can be efficiently solved through interior point methods (IPMs) is embedded into the proposed online controller. Simulations are carried out and validated using GAMS platform and MATLAB/Simulink.

  • 7.
    Nazari, Mohammad
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Application of Multi-Agent Control to Multi-Terminal HVDC Systems2013In: 2013 IEEE Electrical Power & Energy Conference (EPEC), IEEE Computer Society, 2013, p. 6802960-Conference paper (Refereed)
    Abstract [en]

     This paper addresses control of DC voltage in a multi-terminal HVDC system. The proposed control strategy in this paper utilizes multi-agent control methodology to control the set values of the DC voltages of converters. The input data used for the proposed control is based on either local or a combination of local and remote information. For the remote data, a time delay for received information is considered. Some case studies are presented using MATLAB / Simulink to show the performance of the proposed control method.

  • 8.
    Nikjoo, Roya
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
    Estebsari, A.
    Nazari, Mohammad
    KTH.
    Non-parametric Regression Model for Continuous-time Day Ahead Load Forecasting with Bernstein Polynomial2019In: Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8783908Conference paper (Refereed)
    Abstract [en]

    Growing perception of diverse generation resources and demand response operation of power system with high uncertainty has increased the attention to a more dynamic and accurate day-ahead load prediction. In this paper, we develop an stochastic model for short term load forecasting based on the Gaussian process, in which the non parametric estimator of the regression functions are obtained by using Bernstein polynomials. One of the major features of this model is its ability to predict a continuous load at any time of the day with a regression function. We use the historical data for training and the constrained marginal likelihood problem is optimized for finding the hyperparameters of the model. Real data sets from California ISO were used for training and testing the model. The results are compared to the day ahead piecewise constant load and the real time load. The common error measures are employed to infer the deviation of the load forecast from the real data.

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