M.R. Aghamohammadi

Power and Water University of Technology, Teheran, Tehrān, Iran

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Publications (14)3.74 Total impact

  • O. Shariati, A. A. Mohd Zin, A. Khairuddin, M. R. Aghamohammadi
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    ABSTRACT: This paper illustrates a new application of artificial neural network (ANN) observers in identifying and estimating synchronous generator dynamic parameters via time-domain, on-line disturbance measurements. To prepare the training database for an ANN observer, the transient behaviours of synchronous generators have been determined through off-line simulations of a generator operating in a one-machine-infinite-bus environment. The Levenberg–Marquardt optimization utilising very fast back propagation algorithm has been adopted for training feed-forward neural networks. The inputs of ANNs are organized in coordination with the data from the observability analysis of synchronous generator parameters in its dynamic behaviour. A collection of ANNs with same inputs but different outputs is developed to determine a set of the parameters. The ANNs are utilized to estimate the above parameters by the measurements for every kind of fault separately. The robustness tests are executed by on-line measurements to identify the parameters. Simulation studies not only indicate that the observer is capable to identify the dynamic parameters of synchronous generator but also show that the tests which have given better results in identification of each dynamic parameter can be acquired.
    Electrical Engineering 01/2014; · 0.30 Impact Factor
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    ABSTRACT: Non-manageable behaviour of consumers is one of the main causes for low efficiency of conventional power system. Plug in hybrid electric vehicles (PHEV) have been considered as flexible load and generations which can play an important role in the process of demand side management in modern smart grids. Huge collection of PHEVs can be considered as random load or generation which can be managed in such a way to improve system behaviour. For investigating the effects of PHEVs on network performance, different aspect of power system can be studied. In this paper, the effect of PHEVs has been considered in the course of security constrained unit commitment (SCUC). For this purpose, PHEVs have been modelled as manageable load or generation and included in the process of SCUC. By this method, effect of PHEVs on various parameters of the system performance have been studied. In this paper, by modelling hybrid vehicles as a collection of small loads or generators, impacts of these vehicles on the various system performance indices like social welfare, generation costs, amount of not supplied load, and lines loading have been investigated. The simulation studies are carried out for two cases with including line limit and without lines limits. The simulation results show that proper utilization of PHEVs has potential for increasing social welfare and decreasing not supplied load.
    CIGRE 2012 Paris Session; 09/2012
  • Mohammad Reza Aghamohammadi, Ali Shahmohammadi
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    ABSTRACT: Cascading failures and blackouts are the most significant threats for power system security. If the process of cascading failure proceeds by further line tripping, the system will face uncontrolled islanding. Establishment of uncontrolled islands with deficiency in MW or Mvar power balance are the main reasons for system blackout. In order to reduce the risk of blackout due to islanding, intentional or controlled islanding has been considered as a preventive strategy. In this paper, for identifying proper islanding scenarios in network, a new search algorithm based on the ant search mechanism is proposed. The security constraints considered for finding islanding scenarios are load–generation balance and line overloading which are implemented using linear programming and DC load flow. The proposed algorithm has been applied on IEEE 39-bus network with promising results showing the ability of the algorithm for finding proper islanding scenarios quickly.
    International Journal of Electrical Power & Energy Systems - INT J ELEC POWER ENERG SYST. 02/2012;
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    ABSTRACT: The first part of this two part paper has proposed a novel strategy for frequency response modelling of modern power system, as it recommended a new application of artificial neural network in assessment of power system dynamic performance. The intelligent methods have shown a high ability in estimation and optimisation problems, as the recent advances in computer systems and intelligent methods have created new opportunities. The current paper proposes an integrated under frequency load shedding system based on genetic algorithm which is able to consider all effective factors at the same time. It also presents a new hybrid artificial neural network-genetic algorithm basis system for under frequency load shedding which is a quick, simple and applied method of UFLS. This assessment includes a review of significant researches on under frequency load shedding design and application.
    Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific; 01/2012
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    ABSTRACT: Security is one of the most vital requirements in the operation of power systems. Frequency is a reliable indicator to determine instability condition in power system, i.e. the stability of power system is closely dependent on the value of system frequency. Under Frequency Load Shedding (UFLS) is one of the most important protection systems as in many cases it is the last action taken to prevent a system blackout after a serious disturbance occurs in power system. The first part of this two part paper presents various factors in modern power systems which have significant contribution on Under Frequency Load Shedding (UFLS). A high-order multi-machine frequency response model is utilized as it the best strategy of power system dynamic simulation. Classification of modern power system components and using an equal unit for each class is proposed in this work. The results show that ANN models can also be implemented as well as a fast dynamic simulator of electric power system. This assessment includes a review of significant researches on power system dynamic simulation and frequency response model leading to an integrated UFLS system design.
    Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific; 01/2012
  • O. Shariati, A. A. Mohd Zin, M. R. Aghamohammadi
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    ABSTRACT: Parameter identification is critical for modern control strategies in electrical power systems which is considered both dynamic performance and energy efficiency. This paper presents a novel application of ANN observers in estimating and tracking Salient-Pole Synchronous Generator Dynamic Parameters using time-domain, on-line disturbance measurements. The data for training ANN Observers are obtained through off-line simulations of a salient-pole synchronous generator operating in a one-machine-infinite-bus environment. The Levenberg-Marquardt algorithm has been adopted and assimilated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANNs are organized in conformity with the results of the observability analysis of synchronous generator dynamic parameters in its dynamic behavior. A collection of ANNs with same inputs but different outputs are developed to determine a set of the dynamic parameters. The ANNs are employed to estimate the dynamic parameters by the measurements which are carried out within each kind of fault separately. The trained ANNs are tested with on-line measurements to identify the dynamic parameters. Simulation studies indicate the ANN observer has a great ability to identify the dynamic parameters of salient-pole synchronous generator. The results also show that the tests which have given better results in estimation of each dynamic parameter can be obtained.
    01/2011;
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    M.R. Aghamohammadi, S. Hashemi, M.S. Ghazizadeh
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    ABSTRACT: Voltage instability is a major threat for security of power systems. Preserving voltage security margin at a certain limit is a vital requirement for today's power systems. Assessment of voltage security margin is a challenging task demanding sophisticated indices. In this paper, a new index based on the correlation characteristic of network voltage profile is proposed for the purpose of online voltage security assessment. Voltage profile comprising all bus voltages contains the effect of network structure, load-generation patterns and reactive power compensation on the system behaviour and voltage security margin. Therefore, the proposed index is capable to clearly reveal the effect of system characteristics and events on the voltage security margin. The most considerable feature of this index is its fast and easy calculation from synchronously measured voltage profile without any need to system modelling and simulation and without any dependency on network size. At any instant of system operation by merely measuring network voltage profile and no further simulation calculation this index could be evaluated with respect to a specific reference profile. The results show that the behaviour of this index with respect to the change in system security is independent of the selected reference profile. The simplicity and easy calculation make this index very suitable for online application. The proposed approach has been demonstrated on IEEE 39 bus test system with promising results showing its effectiveness and applicability.
    IPEC, 2010 Conference Proceedings; 11/2010
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    F. Mahdloo, M. Manbachi, M.R. Aghamohammadi
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    ABSTRACT: This paper proposed a new method for maximizing the loadability of power networks regardless to the transmission lines compensation types. The paper determines some of the network lines that by making change in the reactance of them, loadability of whole system could rise considerably. This optimized design of mentioned network is performed through Genetic Algorithm (GA) optimization technique. With GA technique, the paper is looking for drastic transmission lines and their compensational values in order to transmitting more power in considered network, provided that maximum loadability of voltage stability besides the minimization of costs is respected sufficiently. Numerical results are obtained applying IEEE-39 Bus test system as a case study for testing correctness and applicability of this new offered solution.
    Modern Electric Power Systems (MEPS), 2010 Proceedings of the International Symposium; 10/2010
  • M. R. Aghamohammadi, S. Hashemi, M. S. Ghazizadeh
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    ABSTRACT: This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profiles of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Voltage profile contains useful information about system stability margin including the effect of load-generation pattern, line outage and reactive power compensation, so it is adopted as the input pattern of VSANN. In fact, VSANN approximates the functional relationship between VSM and the voltage profile. The sensitivity analysis of VSM with respect to reactive power compensation extracted from information stored in the weighting factor of VSANN is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select the most effective buses for reactive power compensation aimed to enhance VSM. The proposed approach has been implemented in IEEE 39-bus test system with promising results showing its effectiveness and applicability.
    01/2010;
  • M.R. Aghamohammadi, A. Maghami, F. Dehghani
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    ABSTRACT: In this paper, a novel approach for generation rescheduling as a preventive control for enhancing dynamic security using neural network is presented.. Critical clearing time (CCT) associated with each fault including the effect of system controllers and limitation, is adopted as dynamic security criteria. A Dynamic Security Analyzer Neural Network (DSANN) is trained to estimate CCTs associated with different system faults. For each given operating point, DSANN evaluate system CCTs by using steady state pre fault operating condition as input pattern. The most interesting feature of the proposed neural network application is evaluation of sensitivity of CCT with respect to generation pattern. These sensitivities are derived from the information stored in the weighting factors of trained DSANN. The sensitivity of CCT is used as a guideline for selecting the most effective pair of generators to reschedule their MW generation in the process of generation rescheduling aimed security enhancement. The proposed method has been demonstrated on the IEEE-39 bus system with promising results for enhancing dynamic security by generation rescheduling using sensitivity characteristic of neural network.
    Power Systems Conference and Exposition, 2009. PSCE '09. IEEE/PES; 04/2009
  • M.R. Aghamohammadi, F. Mahdavizadeh, R. Bagheri
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    ABSTRACT: In this paper a novel approach for transient stability based dynamic security classification and screening of power systems is presented. A Kohenen neural network is implemented as neural network security classifier. The precontingency steady state operating condition of power system is used as the input pattern of the neural network classifier for proper classification of dynamic characteristic. Transient stability is the dynamic behavior by which system security is assessed and classified. Critical clearing time (CCT) is used as security index for both feature extraction and classification of system dynamic security states. For the purpose of feature extraction, correlation between pre contingency operating condition and system dynamic characteristic is used. The proposed approach has been demonstrated on the IEEE-39 bus system with promising results for relatively accurate classification and screening of power system dynamic security.
    Power Systems Conference and Exposition, 2009. PSCE '09. IEEE/PES; 04/2009
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    M R Aghamohammadi, M Pourgholi
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    ABSTRACT: accurate generator modeling allows for more precise calculation of power system control and stability limits. In this paper, a procedure using a set of measured data from Standstill Frequency Response (SSFR) test on MontazerGhaem gas power plant's synchronous generator is used to obtain dynamic parameters of the machine. A novel approach is used to find d-axis which is different from standard SSFR scheme which can save the time in performing SSFR tests. Hook-Jeeves optimization method is used for parameter estimation purpose. The test procedure and identification results are reported.
    09/2007;
  • Sina Hashemi, Mohammad Reza Aghamohammadi
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    ABSTRACT: Online voltage stability assessment is one of the vital requirements for intricate electric power systems. Due to the restructuring and liberalization, modern power systems tend to operate close to their stability limits with small security margin. In such environment, online voltage stability evaluation plays a significant role in secure operation of power systems. This paper presents a new approach for estimating voltage stability margin VSM, based on application of wavelet feature extraction method to network voltage profile. Voltage profile is adopted as the original input data for VSM estimation, because it contains sufficient information concerning network topology, load level, load-generation patterns and all system controllers. In this approach, in order to provide high discrimination between network voltage profiles, Multi-Resolution Wavelet Transform (MRWT) is utilized to extract the features of voltage profiles. Also, in order to eliminate the redundant features, principle component analysis (PCA) is used to select the most relevant features extracted by MRWT. Radial Basis Function (RBF) neural network is adopted to estimate system VSM using the dominant extracted features of the voltage profile by MRWT and PCA. Using voltage profile as the original data makes the proposed approach capable of estimating system VSM in both static and quasi dynamic conditions. The proposed approach has been implemented in New England 39-bus test system with promising results demonstrating its effectiveness and applicability.
    International Journal of Electrical Power & Energy Systems 49:86–94. · 3.43 Impact Factor
  • M R Aghamohammadi, S Hashemi, M S Ghazizadeh
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    ABSTRACT: This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profiles of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Bus voltage profile contains useful information about system stability margin including the effect of load-generation pattern, line outage and reactive power compensation, so it is adopted as input pattern of VSANN. In fact, VSANN establishes a functionality for VSM with respect to voltage profile. Sensitivity analysis of VSM with respect to voltage profile and reactive power compensation extracted from information stored in the weighting factor of VSANN is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select the most effective buses for reactive power compensation aimed enhancing VSM. The proposed approach has been applied to IEEE 39-bus test system which demonstrated applicability of the proposed approach.