A simulated annealing-based optimal controller for a three phase induction motor

This paper presents a new approach for optimal controller design of a three-phase induction motor (IM), based on using the simulated annealing (SA) method to find the optimal controller gains that satisfy a specific performance criterion. Optimal control requires well-known information about the system dynamics, which will preclude its applicability with systems having partially known or unknown dynamics. Accordingly; the proposed approach is implemented to emulate the structure and hence the characteristics of the optimal controller in spite of the partially known system dynamics, inaccuracy or uncertainties of system parameter. The problem is a hard nonlinear optimization problem in continuous variables. An adaptive cooling schedule and a new method for variables discretization are implemented to enhance the speed and convergence of the original simulated annealing algorithm (SAA). The proposed algorithm comprises structure of the optimal controller, a new error system and vector control of a three phase IM. The IM is described as a three input, three output controlled object. The state equations of IM suitable for voltage control are implemented based on the vector, method. Simulation results show better system performance compared to previously obtained results

A PLC based power factor controller for a 3-phase induction motor

This paper proposes a power factor controller (PFC) for a three-phase induction motor (IM), utilizing the programmable logic controller (PLC). This work focuses on the implementation of a laboratory model for a PLC based PFC to improve the power factor of a three-phase induction motor. In addition to keep its voltage to frequency ratio constant in order to maintain a maximum torque over the whole control conditions. During the online process a set of capacitors sized in a binary ratio will be switched on or off with the help of zero voltage static switches according to a control strategy to obtain a pre-specified power factor. This control strategy relies on a look-up table and an expert system. The look-up table is prepared according to a measured value of the phase angle between the stator phase voltage and the stator phase current. Implementation of a software algorithm incorporates measuring the power factor angle, selecting the binary pattern according to the control strategy and sending command signals to switch the appropriate capacitors and protection switches. Zero voltage switching of static switches is also allocated in the control algorithm to prevent the occurrence of the transients, pseudo oscillation and harmonics. Experimental studies have been carried-out for verifying the operation performance of the proposed PFC under different operating conditions. Details of the experimental setup and test results in addition to the recommendations are also demonstrate

"A Global ANN Algorithm for Induction Motor Based On Optimal Preview Control Theory"

In this paper a global Artificial Neural Network (ANN), algorithm for on-line speed control of a threephase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the 1M. The IM model includes thee input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and wnstant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed Ah” using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have ken carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm

"A Global ANN Algorithm for Induction Motor Based On Optimal Preview Control Theory"

In this paper a global Artificial Neural Network (ANN), algorithm for on-line speed control of a threephase induction motor (IM), is proposed. This algorithm is based on the optimal preview controller. It comprises a novel error system and vector control of the 1M. The IM model includes thee input variables, which are the stator angular frequency and the two components of the stator space voltage vector, and three output variables, which are the rotor angular velocity and the two components of the stator space flux linkage. The objective of the proposed algorithm is to achieve rotor speed control, field orientation control and wnstant flux control. In order to emulate the characteristic of the optimal preview controller within global and accurate performance system, a neural network-based technique for the on-line purpose of speed control of IM, is implemented. This technique is utilized based on optimizing the speed control problem using the optimal preview control law. The numerical solution is used to train a feed Ah” using the radial basis method. Successive trained data is utilized to obtain global stability operation for the IM over the whole control intervals. This data includes, several desired speed trajectories and different load torque operations in addition to the motor parameter variations. Digital computer simulation results have ken carried-out to demonstrate the feasibility, reliability and effectiveness of the proposed global ANN algorithm

Correlation between three dimensional multi-slice sonohysterography and hysteroscopy in the diagnosis and classification of submucous myomas

Objective: The aim of our study is to assess the value of three dimensional saline SHG with multi-slice view (3D-MS-SHG) in classifying submucous myomas and to correlate our results with those of diagnostic hysteroscopy. Design and settings: This was a prospective double blind study carried out in the Department of Obstetrics and Gynecology, Cairo University in the period from June 2008 to October 2009. Subjects and methods: Seventy one patients suspected on conventional two dimensional ultrasound of having a submucous myoma considered to be suitable for hysteroscopic resection were recruited. Three dimensional saline SHG with multi-slice view was used to assess the degree of myoma protrusion into the endometrial cavity and to classify the submucous myomas according to the classification adopted by the European Society for Gynaecological Endoscopy (ESGE). These results were then compared with the findings of diagnostic hysteroscopy using Cohen's kappa inter-observer agreement test. Results: A total of 83 myomas were suspected of being submucous by conventional 2D ultrasound in the 71 patients in the study. Of these nine were found to be intramural with no intracavitary extension by 3D-MS-SHG compared to 13 by hysteroscopy. Overall, there was an agreement between 3D-SHG and hysteroscopy in classifying 66/83 myomas (79.5%) with a kappa inter-observer value of 71.5 (95% confidence interval=0.58–0.84). The agreement between the two diagnostic modalities became less as the myometrial portion of the myomas increased. From the 16 myomas diagnosed as Type 0 (fibroid polyps) by hysteroscopy, 14 were suspected as such by 3D-MS-SHG (87.5%), 20/24 (83.3%) of Type I myomas, 23/30 (76.6%) for Type II and 9/13 (69.2%) for intramural myomas with no intracavitary extension. The mean diameter of Type 0 myomas as measured by both 3D-MS-SHG and hysteroscopy was similar (3D-MS-SHG=3.6, hysteroscopy=3.8, p=0.22). However, with increasing myometrial involvement, the discordance between the two modalities increased with a statistically significant difference in the measurement of Type I myomas (3D-MS-SHG=4.5, hysteroscopy=3.6, p=0.0006) as well as Type II myomas (3D-MS-SHG=4.7, hysteroscopy=3.7, p=0.0005). Conclusion: The results of the present study showgood overall agreement between 3D-MC SHG and diagnostic hysteroscopy in classifying submucous myomas with a Cohen's kappa value of 71.5. The highest level of agreement was achieved in classifying Type 0 myomas, becoming more discordant with increasing myometrial involvement

The HCG ratio as a predictor of pregnancy outcome in assisted conception cycles

Objective: To determine whether the HCG ratio can be used to predict pregnancy viability in patients undergoing IVF/ICSI treatment. Design and settings: This was a prospective observational study conducted in a private assisted conception unit. Subjects and methods: The patients recruited had one either a long luteal agonist protocol, a short agonist protocol, or an antagonist protocol. All patients had a maximum of three embryos transferred per cycle. Pregnancy detection was by routine serum HCG measurement on day 14 after oocyte retrieval (HCG 0) followed by another HCG sample 48h later (HCG 48). Patients with an initial positive HCG had a transvaginal ultrasound 14days later to determine viability. Results: Three hundred and twenty patients were included in the study. We used receiver operating characteristics (ROC) analysis to predict the ability of HCG measured at 14days (HCG 0), HCG measured at 16days (HCG 48) after oocyte retrieval as well as the HCG ratio (HCG 48/HCG 0) to predict pregnancy viability as well as to predict multiple pregnancy. The HCG ratio with an optimal cut-off of 1.82 had a sensitivity of 97.6%, a specificity of 98.2% and an area under the ROC curve of 98% in the prediction of pregnancy viability. In the prediction of multiple pregnancy the HCG ratio had an optimal cut-off of 2.06 with a sensitivity of 94.5% and a specificity of only 35.6% and an area under of only the ROC curve of 64%. However, the HCG 0 with a cut-off value of 118.56mIU/ml (sensitivity 97%, specificity 96.5%) and the HCG 48 with a cut-off value of 258.16mIU/ml (sensitivity 97.2%, specificity 99.4%) were shown to be accurate in predicting a viable intrauterine multiple pregnancy with an area under the ROC curve of 97% and 99%, respectively. Conclusion: The HCG ratio with a cut-off value of 1.82 can be used to predict pregnancy viability in assisted conception cycles. Also HCG measured 14 and 16days after oocyte retrieval with a cut-off value of 118.56mIU/ml and 258mIU/ml can be used to predict viable multiple pregnancy

Serum anti-Müllerian hormone and basal serum FSH as predictors of poor ovarian response in assisted conception cycles

Objective: The aim of the study was to correlate serum AMH and serum FSH levels with ovarian response to stimulation in IVF–ICSI cycles. Design and settings: This was a prospective observational study conducted in a private assisted conception unit. Subjects and methods: One hundred and two patients were selected on their first IVF cycle. Basal serum FSH and serum AMH were measured one month before the stimulation cycle. A fixed dose GnRH antagonist protocol was used in all cycles transferring a maximum of three day-3 cleavage stage embryos. We defined poor ovarian response as retrieval of fewer than four mature oocytes in cycles requiring ⩾ 3000 IU of gonadotropins for stimulation or cycle cancellation due to poor response. The correlation between different parameters was expressed as a Spearman’s correlation coefficient. The clinical value of AMH and FSH as predictors of poor ovarian response as well as predictors of pregnancy was evaluated by constructing relevant receiver operator characteristics curves (ROC curves). Results: Of these 102 cycles, 28 fitted our definition of poor response while the remaining 74 cycles all produced an adequate response to stimulation. There was a statistically significant difference between the adequate responders group and poor responders group regarding their mean age (31.5 versus 39.6, p < 0.001), the mean value of AMH (2.84 ng/ml versus 0.9 ng/ml, p < 0.0001) as well as the mean value of basal FSH (7.6 IU/ml versus 9.7 IU/ml, p < 0.0001). Serum AMH level had a positive correlation while serum FSH had a negative correlation with the number of oocytes collected while only serum AMH had a significant positive correlation with the occurrence of pregnancy. ROC curve analysis of our results showed that serum AMH with an optimal cut-off value of 1.2 ng/ml is a reliable predictor of poor ovarian response with an area under the ROC curve of 90.4%. Serum basal FSH with an optimal cut-off value of 8.9 IU/ml was of lower value than AMH as a predictor of poor ovarian response with an area under the ROC curve of 81.9%. However, neither serum AMH nor basal serum FSH was found to able to reliably predict the occurrence of pregnancy with an area under the ROC curve of 59.4% and 58.6% respectively. Conclusion: Our results show that serum AMH level is more reliable than basal serum FSH as a predictor of poor ovarian response to stimulation with a cut-off value of 1.2 ng/ml shown to predict poor ovarian response with a sensitivity of 91.7%