As modern electric power systems are transforming into smart grids, real time wide area monitoring system (WAMS) has become an essential tool for operation and control. With the increasing applications of WAMS for on-line stability analysis and control in smart grids, phasor measurement unit (PMU) is becoming a key element in wide area measurement system and the consequence of the failure of PMU is very severe and may cause a black out. Therefore reliable operation of PMU is very much essential for smooth functioning of the power system. This thesis is focused mainly on evaluating the reliability of PMU using hidden Markov model. Firstly, the probability of given observation sequence is obtained for the individual modules and PMU as a whole using forward and backward algorithm. Secondly, the optimal state sequence each module passes through is found. Thirdly, the parameters of the hidden Markov model are re-estimated using Baum-Welch algorithm.
This slides are the Ph.D. work presentation on Active Power Filter design and implementation for harmonic elimination in micro-grid and electric vehicle
This paper presents a novel shunt active power filter (SAPF). The power converter that is used in this SAPF is constructed from a four-leg asymmetric multi-level cascaded H-bridge (CHB) inverter that is fed from a photovoltaic source. A three-dimensional space vector modulation (3D-SVPWM) technique is adopted in this work. The multi-level inverter can generate 27-level output with harmonic content is almost zero. In addition to the capability to inject reactive power and mitigating the harmonics, the proposed SAPF has also, the ability to inject real power as it is fed from a PV source. Moreover, it has a fault-tolerant capability that makes the SAPF maintaining its operation under a loss of one leg of the multi-level inverter due to an open-circuit fault without any degradation in the performance. The proposed SAPF is designed and simulated in MATLAB SIMULINK using a single nonlinear load and the results have shown a significant reduction in total harmonics distortion (THD) of the source current under the normal operating condition and post a failure in one phase of the SAPF. Also, similar results are obtained when IEEE 15 bus network is used.
What is islanding ?
Consider the power network as shown in fig.1
Now if we disconnect the line AB from the infinite transmission grid there will be an isolated region . The D1, D2 are power sources (eg : inverter , solar power cells ). The power generated in this region is fed to the island only.
We see that there no longer is any control over the island voltage at the bus X . Also there is no mechanism here for control of frequency.
This state is referred to as islanding.
The electrical distribution network is undergoing tremendous modifications with the introduction of distributed generation technologies which have led to an increase in fault current levels in the distribution network. Fault current limiters have been developed as a promising technology to limit fault current levels in power systems. Though, quite a number of fault current limiters have been developed; the most common are the superconducting fault current limiters, solid-state fault current limiters, and saturated core fault current limiters. These fault current limiters present potential fault current limiting solutions in power systems. Nevertheless, they encounter various challenges hindering their deployment and commercialization. This research aimed at designing a bridge-type nonsuperconducting fault current limiter with a novel topology for distribution network applications. The proposed bridge-type nonsuperconducting fault current limiter was designed and simulated using PSCAD/EMTDC. Simulation results showed the effectiveness of the proposed design in fault current limiting, voltage sag compensation during fault conditions, and its ability not to affect the load voltage and current during normal conditions as well as in suppressing the source powers during fault conditions. Simulation results also showed very minimal power loss by the fault current limiter during normal conditions.
Direct current (DC) electronic load is a useful equipment for testing the electrical system. It can emulate various load at a high rating. The electronic load requires a power converter to operate and a linear regulator is a common option. Nonetheless, it is hard to control due to the temperature variation. This paper proposed a DC electronic load using the boost converter. The proposed electronic load operates in the continuous current mode and control using the integral controller. The electronic load using the boost converter is compared with the electronic load using the linear regulator. The results show that the boost converter able to operate as an electronic load with an error lower than 0.5% and response time lower than 13 ms.
Alternating current (AC) electrical drives mainly require smaller current (or torque) ripples and lower total harmonic distortion (THD) of voltage for excellent drive performances. Normally, in practice, to achieve these requirements, the inverter needs to be operated at high switching frequency. By operating at high switching frequency, the size of filter can be reduced. However, the inverter which oftenly employs insulated gate bipolar transistor (IGBT) for high power applications cannot be operated at high switching frequency. This is because, the IGBT switching frequency cannot be operated above 50 kHz due to its thermal restrictions. This paper proposes an alternate switching strategy to enable the use of IGBT for operating the inverter at high switching frequency to improve THD performances. In this strategy, each IGBT in a group of switches in the modified inverter circuit will operate the switching frequency at one-fourth of the inverter switching frequency. The alternate switching is implemented using simple analog and digital integrated circuits.
This slides are the Ph.D. work presentation on Active Power Filter design and implementation for harmonic elimination in micro-grid and electric vehicle
This paper presents a novel shunt active power filter (SAPF). The power converter that is used in this SAPF is constructed from a four-leg asymmetric multi-level cascaded H-bridge (CHB) inverter that is fed from a photovoltaic source. A three-dimensional space vector modulation (3D-SVPWM) technique is adopted in this work. The multi-level inverter can generate 27-level output with harmonic content is almost zero. In addition to the capability to inject reactive power and mitigating the harmonics, the proposed SAPF has also, the ability to inject real power as it is fed from a PV source. Moreover, it has a fault-tolerant capability that makes the SAPF maintaining its operation under a loss of one leg of the multi-level inverter due to an open-circuit fault without any degradation in the performance. The proposed SAPF is designed and simulated in MATLAB SIMULINK using a single nonlinear load and the results have shown a significant reduction in total harmonics distortion (THD) of the source current under the normal operating condition and post a failure in one phase of the SAPF. Also, similar results are obtained when IEEE 15 bus network is used.
What is islanding ?
Consider the power network as shown in fig.1
Now if we disconnect the line AB from the infinite transmission grid there will be an isolated region . The D1, D2 are power sources (eg : inverter , solar power cells ). The power generated in this region is fed to the island only.
We see that there no longer is any control over the island voltage at the bus X . Also there is no mechanism here for control of frequency.
This state is referred to as islanding.
The electrical distribution network is undergoing tremendous modifications with the introduction of distributed generation technologies which have led to an increase in fault current levels in the distribution network. Fault current limiters have been developed as a promising technology to limit fault current levels in power systems. Though, quite a number of fault current limiters have been developed; the most common are the superconducting fault current limiters, solid-state fault current limiters, and saturated core fault current limiters. These fault current limiters present potential fault current limiting solutions in power systems. Nevertheless, they encounter various challenges hindering their deployment and commercialization. This research aimed at designing a bridge-type nonsuperconducting fault current limiter with a novel topology for distribution network applications. The proposed bridge-type nonsuperconducting fault current limiter was designed and simulated using PSCAD/EMTDC. Simulation results showed the effectiveness of the proposed design in fault current limiting, voltage sag compensation during fault conditions, and its ability not to affect the load voltage and current during normal conditions as well as in suppressing the source powers during fault conditions. Simulation results also showed very minimal power loss by the fault current limiter during normal conditions.
Direct current (DC) electronic load is a useful equipment for testing the electrical system. It can emulate various load at a high rating. The electronic load requires a power converter to operate and a linear regulator is a common option. Nonetheless, it is hard to control due to the temperature variation. This paper proposed a DC electronic load using the boost converter. The proposed electronic load operates in the continuous current mode and control using the integral controller. The electronic load using the boost converter is compared with the electronic load using the linear regulator. The results show that the boost converter able to operate as an electronic load with an error lower than 0.5% and response time lower than 13 ms.
Alternating current (AC) electrical drives mainly require smaller current (or torque) ripples and lower total harmonic distortion (THD) of voltage for excellent drive performances. Normally, in practice, to achieve these requirements, the inverter needs to be operated at high switching frequency. By operating at high switching frequency, the size of filter can be reduced. However, the inverter which oftenly employs insulated gate bipolar transistor (IGBT) for high power applications cannot be operated at high switching frequency. This is because, the IGBT switching frequency cannot be operated above 50 kHz due to its thermal restrictions. This paper proposes an alternate switching strategy to enable the use of IGBT for operating the inverter at high switching frequency to improve THD performances. In this strategy, each IGBT in a group of switches in the modified inverter circuit will operate the switching frequency at one-fourth of the inverter switching frequency. The alternate switching is implemented using simple analog and digital integrated circuits.
Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC...Francisco Gonzalez-Longatt
DC microgrids are desired to provide the electricity for the remote areas which are far from the main grid. The microgrid creates the open horizontal environment to interconnect the distributed generation especially photovoltaic (PV). The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the microgrid and forced to introduce the controller for voltage stability. There are many control strategies to control the voltage of a DC microgrid in the literature. In this paper the proportional-integral-derivative (PID) and fuzzy logic PID (FL-PID) controller has been designed and compared in term of performance. Performance measures like maximum overshoot and settling time of FL-PID compared with the PID proved that the former is better controller. The controllers are designed and simulated in the MATLAB programming environment. The controllers has been tested for the real time data obtained from Pecan Street Project, University of Texas at Austin USA.
The inverter is the principal part of the photovoltaic (PV) systems that assures the direct current/alternating current (DC/AC) conversion (PV array is connected directly to an inverter that converts the DC energy produced by the PV array into AC energy that is directly connected to the electric utility). In this paper, we present a simple method for detecting faults that occurred during the operation of the inverter. These types of faults or faults affect the efficiency and cost-effectiveness of the photovoltaic system, especially the inverter, which is the main component responsible for the conversion. Hence, we have shown first the faults obtained in the case of the short circuit. Second, the open circuit failure is studied. The results demonstrate the efficacy of the proposed method. Good monitoring and detection of faults in the inverter can increase the system's reliability and decrease the undesirable faults that appeared in the PV system. The system behavior is tested under variable parameters and conditions using MATLAB/Simulink.
The emerging of inductive wireless power transfer (IWPT) technology provides more opportunities for the electric vehicle (EV) battery to have a better recharging process. With the development of IWPT technology, various way of wireless charging of the EV battery is proposed in order to find the best solution. To further understand the fundamentals of the IWPT system itself, an ample review is done. There are different ways of EV charging which are static charging (wired), static wireless charging (SWC) and dynamic wireless charging (DWC). The review starts with a brief comparison of static charging, SWC and DWC. Then, in detailed discussion on the fundamental concepts, related laws and equations that govern the IWPT principle are also included. In this review, the focus is more on the DWC with a little discussion on static charging and SWC to ensure in-depth understanding before one can do further research about the EV charging process. The in-depth perception regarding the development of DWC is elaborated together with the system architecture of the IWPT and DWC system and the different track versions of DWC, which is installable to the road lane.
This work includes the establishment of a Photovoltaic system connected to the grid by means of an inverter. The fundamental goal of the work is to incorporate an advanced active power flow management scheme in order to adopt load at any weather condition along with the advantage of maximum active power flow and zero harmonics from PV inverter to the grid. The outcome of analysis and control design of grid connected PV inverter using a Proportional-Integral (PI) control technique is based on synchronous dq rotating reference frame so as to achieve maximum output voltage and record the active power. It has been observed that the model provides a better rate of stability as compared to the existing topology.
The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).
An improved luo converter for high power applicationseSAT Journals
Abstract
Power conversion is one of the major requirements in various industries and in daily life. Among various types of power
conversion, DC-DC conversion has greater importance. DC-DC conversion can be reliably performed using luo converter. It
employs voltage lift technique so that output voltage is increased stage by stage, in arithmetic progression. Luo converter can be
incorporated with the Z network or impedance network so as to ensure simple start up and smooth power conversion. An
impedance network consist of two identical inductors and two identical capacitors connected in ‘X’ shape. Besides power
conversion it also offers filtering operation. The luo converter in this scheme is of switched capacitor type. It helps to provide
regulated output voltage from an unregulated source of power supply. The major benefits of this proposed scheme is that it
combines the advantages of the switched capacitor, voltage lift technique and the impedance network. Hence the proposed scheme
has various advantages such as high power density, larger range of output DC voltage, lower or no inrush current, lower
harmonic injection, simple circuit, high voltage transfer gain, can process upto several tens of watts of power. The simulation
analysis and the hardware implementation shows that the output voltage obtained is higher than the expected theoretical value.
i.e, it is the highly boosted voltage output.
Keywords: Z-network, boost voltage, voltage lift technique
This paper deals with an advanced design for a pump powered by solar energyto supply agricultural lands with water and also the maximum power point is used to extract the maximum value of the energy available inside the solar panels and comparing between techniques MPPT such as Incremental conductance, perturb & observe, fractional short current circuit, and fractional open voltage circuit to find the best technique among these. The solar system is designed with main parts: photovoltaic (PV) panel, direct current/direct current (DC/DC) converter, inverter, filter, and in addition, the battery is used to save energy in the event that there is an increased demand for energy and not to provide solar radiation, as well as saving energy in the case of generation more than demand. This work was done using the matrix laboratory (MATLAB) simulink program.
This paper provides a new approach to reducing high-order harmonics in 400 Hz inverter using a three-level neutral-point clamped (NPC) converter. A voltage control loop using the harmonic compensation combined with NPC clamping diode control technology. The capacitor voltage imbalance also causes harmonics in the output voltage. For 400 Hz inverter, maintain a balanced voltage between the two input (direct current) (DC) capacitors is difficult because the pulse width modulation (PWM) modulation frequency ratio is low compared to the frequency of the output voltage. A method of determining the current flowing into the capacitor to control the voltage on the two balanced capacitors to ensure fast response reversal is also given in this paper. The combination of a high-harmonic resonator controller and a neutral-point voltage controller working together on the 400 Hz NPC inverter structure is given in this paper.
An inverter system applied with the PV source typically has a problem of lower input voltage due to constraint in the PV strings connection. As a countermeasure a DC-DC boost converter is placed in between to achieve a higher voltage at the inverter DC link for connection to the grid and to realize the MPPT operation. This additional stage contributes to losses and complexity in control thus reducing the overall system efficiency. This work discussed on the design and development of a grid-connected quasi-Z-source PV inverter which has different topology and control method compared to the conventional voltage source inverter and able to overcome the above disadvantages. Modelling and performance analysis of the voltage and current controller to achieve a good power transfer from the PV source, as well sycnchronization with the grid are presented in detail. Results from both simulation and experimental verification demonstrate the designed and developed grid-connected qZSI PV inverter works successfully equivalent to the conventional voltage source inverter system.
Solar energy based impedance-source inverter for grid systemIJECEIAES
In this work, the fickleness of solar energy can be overcome by using Maximum Power Point Tracking algorithm (MPPT). Perturb and Observation (P&O) MPPT algorithm accomplish fast the maximum power point for rapid change of environmental conditions such as irradiance intensity and temperature. The MPPT algorithm applied to solar PV system keep the boost converter output constant. Output from boost converter is taken to three phase impedance-source inverter with RL load and grid system. Impedance-source inverter performs the transformation of variable DC output of the solar PV system in to near sinusoidal AC output. This near sinusoidal AC output consecutively is served to the RL load first and then to grid system. The simulation is carried out in matlab/simulink platform both for RL load and grid system and the simulation results are experimentally validated for RL load arrangement only.
This study investigates experimentally the performance of two-dimensional solar tracking systems with reflector using commercial silicon based photovoltaic module, with open and closed loop control systems. Different reflector materials were also investigated. The experiments were performed at the Hashemite University campus in Zarqa at a latitude of 32⁰, in February and March. Photovoltaic output power and performance were analyzed. It was found that the modified photovoltaic module with mirror reflector generated the highest value of power, while the temperature reached a maximum value of 53 ̊ C. The modified module suggested in this study produced 5% more PV power than the two-dimensional solar tracking systems without reflector and produced 12.5% more PV power than the fixed PV module with 26⁰ tilt angle.
Electricity is a major source of energy for fast growing population and the use of nonrenewable source is harmful for our environment. This reason belongs to devastating of environment, so it is required to take immediate action to solve these problems which result the solar energy development. Production of a solar energy can be maximizing if we use solar follower. The major part of solar panels is microcontroller with arrangement of LDR sensor is used to follow the sun, where the sensors is less efficient to track the sun because of the low sensitivity of LDR. We are proposing a method to track sun more effetely with the help of both LDR sensors and image processing. This type of mechanism can track sun with the help of image processing software which combines both result of sensors and processed sun image to control the solar panel. The combination of both software and hardware can control thousands of solar panels in solar power plants.
Harvesting energy from the sun makes the photovoltaic (PV) power generation a promising technology. To obtain a consistent state of charge (SOC), consistent energy must be harvested and efficiently directed to the battery. Overcharging or undercharging phenomena decreases the lifetime of the battery. Besides, the effect of irradiance toward solar in term of sunlight intensity effects the efficiency and hence, sluggish the SOC. The main problem of the solar panel revealed when the temperature has increased, the efficiency of solar panel will also be decreased. This manuscript reports the finding of developing an automatic active cooling system for a solar panel with a real time energy monitoring system with internet-of-things (IoT) facility. The IoT technology assists user to measure the efficiency of the solar panel and SOC of the battery in real time from any locations. The automatic active cooling system is designed to improve the efficiency of the solar panel. The effectiveness of the proposed system is proven via the analysis of the effect of active cooling toward efficiency and SOC of photovoltaic system. The results also tabulate the comparative studies of active-to-passive cooling system, as well as the effect of cooling towards SOC and efficiency of the solar panel.
When the irradiance distribution over the photovoltaic panels is uniform, the pursuit of the maximum power point is not reached, which has allowed several researchers to use traditional MPPT techniques to solve this problem Among these techniques a PSO algorithm is used to have the maximum global power point (GMPPT) under partial shading. On the other hand, this one is not reliable vis-à-vis the pursuit of the MPPT. Therefore, in this paper we have treated another technique based on a new modified PSO algorithm so that the power can reach its maximum point. The PSO algorithm is based on the heuristic method which guarantees not only the obtaining of MPPT but also the simplicity of control and less expensive of the system. The results are obtained using MATLAB show that the proposed modified PSO algorithm performs better than conventional PSO and is robust to different partial shading models.
These slides present the maximum power point tracking (MPPT ) algorithms for solar (PV) systems. Later of the class we will discuss on MPPT control of wind generators.
A new bidirectional multilevel inverter topology with a high number of voltage levels with a very reduced number of power components is proposed in this paper. Only TEN power switches and four asymmetric DC voltage sources are used to generate 25 voltage levels in this new topology. The proposed multilevel converter is more suitable for e-mobility and photovoltaic applications where the overall energy source can be composed of a few units/associations of several basic source modules. Several benefits are provided by this new topology: Highly sinusoidal current and voltage waveforms, low Total Harmonic Distortion, very low switching losses, and minimum cost and size of the device. For optimum control of this 25-level voltage inverter, a special Modified Hybrid Modulation technique is performed. The proposed 25-level inverter is compared to various topologies published recently in terms of cost, the number of active power switches, clamped diodes, flying capacitors, DC floating capacitors, and the number of DC voltage sources. This comparison clearly shows that the proposed topology is cost-effective, compact, and very efficient. The effectiveness and the good performance of the proposed multilevel power converter (with and without PWM control) are verified and checked by computational simulations.
Environmental factors such as air pollution and increase in global warming by using polluting fuels are the most important reasons of using renewable and clean energy that runs in global community. Wind energy is one of the most suitable and widely used kind of renewable energy which had been in consideration so well. This paper introduces an electric power generation
system of wind based on Y-source and improved Y-source inverter to deliver optimal electrical power to the network. This new converter is from impedance source converters family. This presented converter has more degrees of freedom to adjust voltage gain and modulation. Also, by limiting the range of simultaneous control (shooting through) while it maintains the
highest power of maximizer, it can operate in higher modulation range. This causes the reduce of stress in switching and thus it will improve the quality of output. Recommended system had been simulated in MATLAB/Simulink and shown results indicate accurate functionality.
Effect of Phasor Measurement Unit (PMU) on the Network Estimated VariablesIDES Editor
The classical method of power measurement of a
system are iterative and bulky in nature. The new technique
of measurement for bus voltage, bus current and power flow is
a Phasor Measurement Unit. The classical technique and PMUs
are combined with full weighted least square state estimator
method of measurement will improves the accuracy of the
measurement. In this paper, the method of combining Full
weighted least square state estimation method and classical
method incorporation with PMU for measurement of power
will be investigated. Some cases are tested in view of accuracy
and reliability by introducing of PMUs and their effect on
variables like power flows are illustrated. The comparison of
power obtained on each bus of IEEE 9 and IEEE 14 bus system
will be discussed.
Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC...Francisco Gonzalez-Longatt
DC microgrids are desired to provide the electricity for the remote areas which are far from the main grid. The microgrid creates the open horizontal environment to interconnect the distributed generation especially photovoltaic (PV). The stochastic nature of the PV output power introduces the large fluctuations of the power and voltage in the microgrid and forced to introduce the controller for voltage stability. There are many control strategies to control the voltage of a DC microgrid in the literature. In this paper the proportional-integral-derivative (PID) and fuzzy logic PID (FL-PID) controller has been designed and compared in term of performance. Performance measures like maximum overshoot and settling time of FL-PID compared with the PID proved that the former is better controller. The controllers are designed and simulated in the MATLAB programming environment. The controllers has been tested for the real time data obtained from Pecan Street Project, University of Texas at Austin USA.
The inverter is the principal part of the photovoltaic (PV) systems that assures the direct current/alternating current (DC/AC) conversion (PV array is connected directly to an inverter that converts the DC energy produced by the PV array into AC energy that is directly connected to the electric utility). In this paper, we present a simple method for detecting faults that occurred during the operation of the inverter. These types of faults or faults affect the efficiency and cost-effectiveness of the photovoltaic system, especially the inverter, which is the main component responsible for the conversion. Hence, we have shown first the faults obtained in the case of the short circuit. Second, the open circuit failure is studied. The results demonstrate the efficacy of the proposed method. Good monitoring and detection of faults in the inverter can increase the system's reliability and decrease the undesirable faults that appeared in the PV system. The system behavior is tested under variable parameters and conditions using MATLAB/Simulink.
The emerging of inductive wireless power transfer (IWPT) technology provides more opportunities for the electric vehicle (EV) battery to have a better recharging process. With the development of IWPT technology, various way of wireless charging of the EV battery is proposed in order to find the best solution. To further understand the fundamentals of the IWPT system itself, an ample review is done. There are different ways of EV charging which are static charging (wired), static wireless charging (SWC) and dynamic wireless charging (DWC). The review starts with a brief comparison of static charging, SWC and DWC. Then, in detailed discussion on the fundamental concepts, related laws and equations that govern the IWPT principle are also included. In this review, the focus is more on the DWC with a little discussion on static charging and SWC to ensure in-depth understanding before one can do further research about the EV charging process. The in-depth perception regarding the development of DWC is elaborated together with the system architecture of the IWPT and DWC system and the different track versions of DWC, which is installable to the road lane.
This work includes the establishment of a Photovoltaic system connected to the grid by means of an inverter. The fundamental goal of the work is to incorporate an advanced active power flow management scheme in order to adopt load at any weather condition along with the advantage of maximum active power flow and zero harmonics from PV inverter to the grid. The outcome of analysis and control design of grid connected PV inverter using a Proportional-Integral (PI) control technique is based on synchronous dq rotating reference frame so as to achieve maximum output voltage and record the active power. It has been observed that the model provides a better rate of stability as compared to the existing topology.
The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).
An improved luo converter for high power applicationseSAT Journals
Abstract
Power conversion is one of the major requirements in various industries and in daily life. Among various types of power
conversion, DC-DC conversion has greater importance. DC-DC conversion can be reliably performed using luo converter. It
employs voltage lift technique so that output voltage is increased stage by stage, in arithmetic progression. Luo converter can be
incorporated with the Z network or impedance network so as to ensure simple start up and smooth power conversion. An
impedance network consist of two identical inductors and two identical capacitors connected in ‘X’ shape. Besides power
conversion it also offers filtering operation. The luo converter in this scheme is of switched capacitor type. It helps to provide
regulated output voltage from an unregulated source of power supply. The major benefits of this proposed scheme is that it
combines the advantages of the switched capacitor, voltage lift technique and the impedance network. Hence the proposed scheme
has various advantages such as high power density, larger range of output DC voltage, lower or no inrush current, lower
harmonic injection, simple circuit, high voltage transfer gain, can process upto several tens of watts of power. The simulation
analysis and the hardware implementation shows that the output voltage obtained is higher than the expected theoretical value.
i.e, it is the highly boosted voltage output.
Keywords: Z-network, boost voltage, voltage lift technique
This paper deals with an advanced design for a pump powered by solar energyto supply agricultural lands with water and also the maximum power point is used to extract the maximum value of the energy available inside the solar panels and comparing between techniques MPPT such as Incremental conductance, perturb & observe, fractional short current circuit, and fractional open voltage circuit to find the best technique among these. The solar system is designed with main parts: photovoltaic (PV) panel, direct current/direct current (DC/DC) converter, inverter, filter, and in addition, the battery is used to save energy in the event that there is an increased demand for energy and not to provide solar radiation, as well as saving energy in the case of generation more than demand. This work was done using the matrix laboratory (MATLAB) simulink program.
This paper provides a new approach to reducing high-order harmonics in 400 Hz inverter using a three-level neutral-point clamped (NPC) converter. A voltage control loop using the harmonic compensation combined with NPC clamping diode control technology. The capacitor voltage imbalance also causes harmonics in the output voltage. For 400 Hz inverter, maintain a balanced voltage between the two input (direct current) (DC) capacitors is difficult because the pulse width modulation (PWM) modulation frequency ratio is low compared to the frequency of the output voltage. A method of determining the current flowing into the capacitor to control the voltage on the two balanced capacitors to ensure fast response reversal is also given in this paper. The combination of a high-harmonic resonator controller and a neutral-point voltage controller working together on the 400 Hz NPC inverter structure is given in this paper.
An inverter system applied with the PV source typically has a problem of lower input voltage due to constraint in the PV strings connection. As a countermeasure a DC-DC boost converter is placed in between to achieve a higher voltage at the inverter DC link for connection to the grid and to realize the MPPT operation. This additional stage contributes to losses and complexity in control thus reducing the overall system efficiency. This work discussed on the design and development of a grid-connected quasi-Z-source PV inverter which has different topology and control method compared to the conventional voltage source inverter and able to overcome the above disadvantages. Modelling and performance analysis of the voltage and current controller to achieve a good power transfer from the PV source, as well sycnchronization with the grid are presented in detail. Results from both simulation and experimental verification demonstrate the designed and developed grid-connected qZSI PV inverter works successfully equivalent to the conventional voltage source inverter system.
Solar energy based impedance-source inverter for grid systemIJECEIAES
In this work, the fickleness of solar energy can be overcome by using Maximum Power Point Tracking algorithm (MPPT). Perturb and Observation (P&O) MPPT algorithm accomplish fast the maximum power point for rapid change of environmental conditions such as irradiance intensity and temperature. The MPPT algorithm applied to solar PV system keep the boost converter output constant. Output from boost converter is taken to three phase impedance-source inverter with RL load and grid system. Impedance-source inverter performs the transformation of variable DC output of the solar PV system in to near sinusoidal AC output. This near sinusoidal AC output consecutively is served to the RL load first and then to grid system. The simulation is carried out in matlab/simulink platform both for RL load and grid system and the simulation results are experimentally validated for RL load arrangement only.
This study investigates experimentally the performance of two-dimensional solar tracking systems with reflector using commercial silicon based photovoltaic module, with open and closed loop control systems. Different reflector materials were also investigated. The experiments were performed at the Hashemite University campus in Zarqa at a latitude of 32⁰, in February and March. Photovoltaic output power and performance were analyzed. It was found that the modified photovoltaic module with mirror reflector generated the highest value of power, while the temperature reached a maximum value of 53 ̊ C. The modified module suggested in this study produced 5% more PV power than the two-dimensional solar tracking systems without reflector and produced 12.5% more PV power than the fixed PV module with 26⁰ tilt angle.
Electricity is a major source of energy for fast growing population and the use of nonrenewable source is harmful for our environment. This reason belongs to devastating of environment, so it is required to take immediate action to solve these problems which result the solar energy development. Production of a solar energy can be maximizing if we use solar follower. The major part of solar panels is microcontroller with arrangement of LDR sensor is used to follow the sun, where the sensors is less efficient to track the sun because of the low sensitivity of LDR. We are proposing a method to track sun more effetely with the help of both LDR sensors and image processing. This type of mechanism can track sun with the help of image processing software which combines both result of sensors and processed sun image to control the solar panel. The combination of both software and hardware can control thousands of solar panels in solar power plants.
Harvesting energy from the sun makes the photovoltaic (PV) power generation a promising technology. To obtain a consistent state of charge (SOC), consistent energy must be harvested and efficiently directed to the battery. Overcharging or undercharging phenomena decreases the lifetime of the battery. Besides, the effect of irradiance toward solar in term of sunlight intensity effects the efficiency and hence, sluggish the SOC. The main problem of the solar panel revealed when the temperature has increased, the efficiency of solar panel will also be decreased. This manuscript reports the finding of developing an automatic active cooling system for a solar panel with a real time energy monitoring system with internet-of-things (IoT) facility. The IoT technology assists user to measure the efficiency of the solar panel and SOC of the battery in real time from any locations. The automatic active cooling system is designed to improve the efficiency of the solar panel. The effectiveness of the proposed system is proven via the analysis of the effect of active cooling toward efficiency and SOC of photovoltaic system. The results also tabulate the comparative studies of active-to-passive cooling system, as well as the effect of cooling towards SOC and efficiency of the solar panel.
When the irradiance distribution over the photovoltaic panels is uniform, the pursuit of the maximum power point is not reached, which has allowed several researchers to use traditional MPPT techniques to solve this problem Among these techniques a PSO algorithm is used to have the maximum global power point (GMPPT) under partial shading. On the other hand, this one is not reliable vis-à-vis the pursuit of the MPPT. Therefore, in this paper we have treated another technique based on a new modified PSO algorithm so that the power can reach its maximum point. The PSO algorithm is based on the heuristic method which guarantees not only the obtaining of MPPT but also the simplicity of control and less expensive of the system. The results are obtained using MATLAB show that the proposed modified PSO algorithm performs better than conventional PSO and is robust to different partial shading models.
These slides present the maximum power point tracking (MPPT ) algorithms for solar (PV) systems. Later of the class we will discuss on MPPT control of wind generators.
A new bidirectional multilevel inverter topology with a high number of voltage levels with a very reduced number of power components is proposed in this paper. Only TEN power switches and four asymmetric DC voltage sources are used to generate 25 voltage levels in this new topology. The proposed multilevel converter is more suitable for e-mobility and photovoltaic applications where the overall energy source can be composed of a few units/associations of several basic source modules. Several benefits are provided by this new topology: Highly sinusoidal current and voltage waveforms, low Total Harmonic Distortion, very low switching losses, and minimum cost and size of the device. For optimum control of this 25-level voltage inverter, a special Modified Hybrid Modulation technique is performed. The proposed 25-level inverter is compared to various topologies published recently in terms of cost, the number of active power switches, clamped diodes, flying capacitors, DC floating capacitors, and the number of DC voltage sources. This comparison clearly shows that the proposed topology is cost-effective, compact, and very efficient. The effectiveness and the good performance of the proposed multilevel power converter (with and without PWM control) are verified and checked by computational simulations.
Environmental factors such as air pollution and increase in global warming by using polluting fuels are the most important reasons of using renewable and clean energy that runs in global community. Wind energy is one of the most suitable and widely used kind of renewable energy which had been in consideration so well. This paper introduces an electric power generation
system of wind based on Y-source and improved Y-source inverter to deliver optimal electrical power to the network. This new converter is from impedance source converters family. This presented converter has more degrees of freedom to adjust voltage gain and modulation. Also, by limiting the range of simultaneous control (shooting through) while it maintains the
highest power of maximizer, it can operate in higher modulation range. This causes the reduce of stress in switching and thus it will improve the quality of output. Recommended system had been simulated in MATLAB/Simulink and shown results indicate accurate functionality.
Effect of Phasor Measurement Unit (PMU) on the Network Estimated VariablesIDES Editor
The classical method of power measurement of a
system are iterative and bulky in nature. The new technique
of measurement for bus voltage, bus current and power flow is
a Phasor Measurement Unit. The classical technique and PMUs
are combined with full weighted least square state estimator
method of measurement will improves the accuracy of the
measurement. In this paper, the method of combining Full
weighted least square state estimation method and classical
method incorporation with PMU for measurement of power
will be investigated. Some cases are tested in view of accuracy
and reliability by introducing of PMUs and their effect on
variables like power flows are illustrated. The comparison of
power obtained on each bus of IEEE 9 and IEEE 14 bus system
will be discussed.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Voltage Stability Assessment using Phasor Measurement Units in Power Network ...Satyendra Singh
This paper presents the assessment methodology for
voltage stability using Phasor Measurement Unit (PMU) with
complete system observability. For full power system
observability, the PMU placement is considered with and without
conventional power flow as well as injection measurement such
that minimum number of PMU’s is used. Data obtained by
PMU’s are used for voltage stability assessment with the help of
L-Index. As the PMU gives real time voltage and current phasors
and L-index is dependent on voltage and admittance values, thus
the L-index so obtained can be used as real time voltage stability
indicator. The case study has been carried out on IEEE-14 bus
system.
Joint State and Parameter Estimation by Extended Kalman Filter (EKF) techniqueIJERD Editor
In order to increase power system stability and reliability during and after disturbances, power grid
global and local controllers must be developed. SCADA system provides steady and low sampling density. To
remove these limitation PMUs are being rapidly adopted worldwide. Dynamic states of power system can be
estimated using EKF. This requires field excitation as input which may not available. As a result, the EKF with
unknown inputs proposed for identifying and estimating the states and the unknown inputs of the synchronous
machine.
A Survey On Real Time State Estimation For Optimal Placement Of Phasor Measur...IJSRD
The traditional methods of security assessment using offline data and SCADA data have become inconsistent for real time operations. The latest and propelled strategy in electric power system used for security assessment is “synchrophasor†measurement technique. The device called Phasor measurement unit (PMU) provides the time stamped data for proper monitoring, control and protection of the power system. PMU measures positive sequence voltage and current time synchronized to within a microsecond. The time synchronization of data is done with the help of timing signals from Global Positioning System (GPS). However, Phasor measurements units cannot be placed on every bus in a network mainly because of economical constraints. In this paper we provide a literature survey of determining the minimum number of Phasor measurement units to be placed in a given network so that the system becomes observable.
These slides are all about Phasor Measurement Units (PMUs). An introduction to PMU is presented as a preliminary knowledge for the course 'Distribution Generation and Smart Grid'. Your valuable suggestions are welcome.
The phasor measurement unit (PMU) which is actually a key tool in providing situational awareness, operation and reliability of the power system network.
These slides focus on preliminary discussions about wide area monitoring, protection and control in future smart grid. Later in the class i will show its application through simulation and case study results.
Series of blackouts encountered in recent years in power system have been occurred because either of voltage or angle instability or both together was not detected within time and progressive voltage or angle instability further degraded the system condition, because of increase in loading. This paper presents the real-time assessment methodology of voltage stability using Phasor Measurement Unit (PMU) with observability of load buses only in power network. PMUs are placed at strategically obtained location such that minimum number of PMU’s can make all load buses observable. Data obtained by PMU’s are used for voltage stability assessment with the help of successive change in the angle of bus voltage with respect to incremental load, which is used as on-line voltage stability predictor (VSP). The real-time voltage phasors obtained by PMU’s are used as real time voltage stability indicator. The case study has been carried out on IEEE-14 bus system and IEEE-30 bus systems to demonstrate the results.
A Fault Detection and Classification Method for SC Transmission Line Using Ph...paperpublications3
Abstract: In this paper, fault detection and classification for Series Compensated Line (SCL) using phasor measurement unit is presented. The algorithm presented in this paper uses the PMU synchronized measurements and not depends on the data to be provided by the electricity utility. The compensated line parameters and Thevenin’s equivalent (TE) of the system at SCL terminals are calculated online, using three independent sets of pre-fault phasor measurements. The accuracy of fault location is performed with respect to fault location/position, types of fault, fault angle. The accuracy of the algorithm is simulated in MATLAB for 9-bus transmission system.
Ijeee 28-32-accurate fault location estimation in transmission linesKumar Goud
Accurate Fault Location Estimation in Transmission Lines
B. Narsimha Reddy Dr. P. Chandra Sekar
Sr. Assistant Professor, Dept. of EEE Associate Professor, Dept. of EEE
Mahatma Gandhi Institute of Technology Mahatma Gandhi Institute of Technology
Hyderabad, TS, India Hyderabad, TS, India
babubnr@gmail.com Pcs_76@rediffmail.com
Abstract: In trendy power transmission systems, the double-circuit line structure is increasingly adopted. However, owing to the mutual coupling between the parallel lines it is quite difficult to style correct fault location algorithms. Moreover, the widely used series compensator and its protecting device introduce harmonics and non-linearity’s to the transmission lines, that create fault location a lot of difficult. To tackle these issues, this thesis is committed to developing advanced fault location strategies for double-circuit and series-compensated transmission lines. Algorithms utilizing thin measurements for pinpointing the situation of short-circuit faults on double-circuit lines square measure planned. By moldering the initial net-work into 3 sequence networks, the bus ohmic resistance matrix for every network with the addition of the citations fault bus may be developed. It’s a perform of the unknown fault location. With the increased bus ohmic resistance matrices the sequence voltage amendment throughout the fault at any bus may be expressed in terms of the corresponding sequence fault current and also the transfer ohmic resistance between the fault bus and the measured bus. Resorting to tape machine the superimposed sequence current at any branch may be expressed with relevancy the pertaining sequence fault current and transfer ohmic resistance terms. Obeying boundary conditions of different fault sorts, four different categories of fault location algorithms utilizing either voltage phasors, or phase voltage magnitudes, or current phasors or section current magnitudes square measure derived. The distinguishing characteristic of the planned methodology is that the information measurements need not stem from the faulted section itself. Quite satisfactory results are obtained victimisation EMTP simulation studies. A fault location rule for series-compensated transmission lines that employs two-terminal asynchronous voltage and current measurements has been implemented. For the distinct cases that the fault happens either on the left or on the right aspect of the series compensator, 2 subroutines square measure developed. In addition, the procedure to spot the proper fault location estimate is represented during this work. Simulation studies disbursed with Matlab Sim Power Systems show that the fault location results square measure terribly correct.
Keywords: Ohmic Resistance, Transmission Lines, PMU, DFR, VCR, EMTP, MOV.
"Use of PMU data for locating faults and mitigating cascading outage"Power System Operation
Large number of Phasor Measurement Units (PMUs)
as a part of the system-wide synchrophasor monitoring
system are being deployed in the USA and around
the world. Transmission Operators (TOs), as well as
Independent system Operators (ISOs) or Regional
Transmission Operators (RTOs) are looking at leveraging
this high-resolution data to improve their ability to
monitor and control the grid. This paper elaborates on
the use of PMU data to locate faults in the power system,
and provides a prediction method for monitoring how the
system behaves after complex switching actions caused
by cascading events. A method for arresting cascades by
performing controlled islanding is also proposed.
We first present a novel system-wide fault location
method for transmission lines utilizing electromechanical
wave propagation phenomena. The method uses
synchrophasor measurements during disturbances
obtained from phasor measurement units sparsely
located in the network. The method determines the time-
of-arrival of electromechanical waves propagating from
the fault point to sparsely located PMUs. By taking the
speed of electromechanical wave propagation as well
as topology of the network into account, the method is
able to detect the faulty line. Finally, by adding fictitious
buses inside faulty line and applying binary search
method, location of fault is accurately pinpointed. The
main advantage of the proposed method is the use of
limited number of existing PMUs, which reduces the
cost of implementation. Test results reveal the high
accuracy of the method in detecting and locating faults.
Early prediction of cascade events outages followed
by immediate and proper control actions can prevent
Phasor measurement unit and it's application pptKhurshid Parwez
The effective operation of power systems in the present and the future depends to a large extent on how well the emerging challenges are met today. Power systems continue to be stressed as they are operated in many instances at or near their full capacities. In order to keep power systems operating in secure and economic conditions, it is necessary to further improve power system protection and control system. Phasor measurement unit (PMUs), introduced into power system as a useful tool for monitoring the performance of power system, has been proved its value in the extensive applications of electric power system. In response, a research program that is specifically aimed at using PMU to improve the power system protection and control. To ensure that the proposed research program is responsive to particular industry needs in this area, and participants of the workshop identified two major research areas in which technological and institutional solutions are needed: 1) PMU implementation, 2) PMU applications. It’s recommends research, design, and development (RD&D) projects in this report. The objective of these projects is to improve the reliability of local and wide transmission grid by enabling and enhancing the system protection and control schemes by using PMU measurement data, reduce the economic burden of utilizes to implement PMUs.
A Novel Back Up Wide Area Protection Technique for Power Transmission Grids U...Power System Operation
Current differential protection relays are widely applied
to the protection of electrical plant due to their simplicity,
sensitivity and stability for internal and external faults. The proposed
idea has the feature of unit protection relays to protect large
power transmission grids based on phasor measurement units. The
principle of the protection scheme depends on comparing positive
sequence voltage magnitudes at each bus during fault conditions
inside a system protection center to detect the nearest bus to
the fault. Then the absolute differences of positive sequence current
angles are compared for all lines connecting to this bus to
detect the faulted line. The new technique depends on synchronized
phasor measuring technology with high speed communication
system and time transfer GPS system. The simulation of the interconnecting
system is applied on 500 kV Egyptian network using
Matlab Simulink. The new technique can successfully distinguish
between internal and external faults for interconnected lines. The
new protection scheme works as unit protection system for long
transmission lines. The time of fault detection is estimated by 5
msec for all fault conditions and the relay is evaluated as a back
up relay based on the communication speed for data transferring.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Reliability analysis of pmu using hidden markov model
1. International Journal Of Scientific Research And Education
||Volume||3||Issue||4||Pages-3210-3238||April-2015|| ISSN (e): 2321-7545
Website: http://ijsae.in
Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3210
Reliability Analysis of Phasor Measurement Unit Using Hidden MARKOV Model
Author
Amaresh Choudhury
Email-amaresh.mitu@gmail.com
ABSTRACT
As modern electric power systems are transforming into smart grids, real time wide area monitoring system
(WAMS) has become an essential tool for operation and control. With the increasing applications of WAMS for
on-line stability analysis and control in smart grids, phasor measurement unit (PMU) is becoming a key
element in wide area measurement system and the consequence of the failure of PMU is very severe and may
cause a black out. Therefore reliable operation of PMU is very much essential for smooth functioning of the
power system. This thesis is focused mainly on evaluating the reliability of PMU using hidden Markov model.
Firstly, the probability of given observation sequence is obtained for the individual modules and PMU as a
whole using forward and backward algorithm. Secondly, the optimal state sequence each module passes
through is found. Thirdly, the parameters of the hidden Markov model are re-estimated using Baum-Welch
algorithm.
Index Terms—Hidden Markov Model (HMM), Phasor Measurement Unit, Reliability, wide area monitoring
system (WAMS), Viterbi Algorithm, Baum-Welch Algorithm.
INTRODUCTION
The interest in phasor measurement technology has reached a peak in recent years, as the need for the best
estimate of the power system's state is recognized to be a crucial element in improving its performance and its
resilience in the face of catastrophic failures. All installations are reaching for a hierarchical Wide-area
measurement system (WAMS ) so that the measurements obtained from various substations on the system can
be collected at central locations from which various monitoring, protection, and control applications can be
developed. Wide Area Measurement System(WAMS) [1-2] is the advanced technology used to avoid major
regional blackouts as those occurred in North America and Canada in 2003.WAMS facilitates the continuous
and synchronous monitoring of power system . Phasor Measurement Unit (PMU) is the key component in
WAMS, which provides GPS synchronization, synchronized phasor voltages, currents, frequency and rate of
2. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3211
change of frequency. Advantages of GPS system is that it provides accuracy of 1µs i.e. 0.0210
for 50 Hz
system and 0.0180
for 60 Hz system.
OBJECTIVE OF THE PAPER
Reliability Analysis of PMU using Hidden Markov Model:
The probability of given observation sequence is obtained for the individual modules and PMU as a whole
using forward algorithm.
The optimal state sequence of each module passes through is found.
The parameters of the hidden Markov model are re-estimated using Baum-Welch algorithm.
HISTORICAL OVERVIEW
Phase angles of voltage phasors of power network buses have always been of special interest to power system
engineers. It is well known that active (real) power flow in a power line is very nearly proportional to sine of
the angle difference between voltages at the two terminals of the line. As many of the planning and operational
considerations in a power network are directly concerned with the flow of real power, measuring angle
differences across transmission has been of concern for many years. The earliest modern application involving
direct measurement of phase angle difference, these systems used LORAN-C, GOES satellite transmissions,
and the HBG radio transmissions(in Europe) in order to obtain synchronization of reference time at different
locations in a power system. The next level available positive going zero crossing of a phase voltage was used
to estimate the local phase angle with respect to the time reference. Using the difference of the measured angles
on a common reference at two locations, the phase angle difference between voltages at two buses was
established. Measurement accuracies achieved in these systems were of the order of 40 µs. Single phase voltage
angles were measured and of course no attempt was made to measure the prevailing voltage phasor magnitude.
Neither was any account taken of the harmonics contained in the voltage waveform. These methods of
measuring phase angle differences are not suitable for generalization for wide area phasor measurement systems
and remain one of a kind system which are no longer in use.
The modern area of phasor measurement technology has its genesis in research conducted on computer relaying
of transmission lines. Early work on transmission line relaying with microprocessor based relays showed that
the available computer power in those days was barely sufficient to manage the calculations needed to perform
all the transmission line relaying functions.
3. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3212
A significant portion of the computations was dedicated to solving six fault loop equations at each sample time
in order to determine if any one of the ten types of faults possible on a three phase transmission lines are
present. The search for methods which would eliminate the need to solve the six equations finally yielded a new
relaying technique which was based on symmetrical component analysis of line voltages and currents. Using
symmetrical components and certain quantities derived from them, it was possible to perform all fault
calculations with single equation. Efficient algorithms for computing symmetrical components of three phase
voltages and currents were described and calculation of positive sequence voltages and currents using the
algorithms gave an impetus for the development of modern phasor measurement systems. Positive sequence
voltages of a network constitute the state vector of a power system, and it is of fundamental importance in all of
power system analysis. The Global Positioning System (GPS) was beginning to be fully deployed around that
time. It became clear that this system offered the most effective way of synchronizing power system
measurements over great distances. The first prototypes of the modern “phasor measurement units” (PMUs)
using GPS were built at Virginia Tech in early 1980s, and two of these prototypes are shown in fig 1.The
Prototype PMU units built at Virginia Tech were deployed at a few substations of the Bonneville Power
Administration, The American Electric Power Service Corporation, and the New York Power Authority. The
first commercial manufacture of PMUs with Virginia Tech collaboration was started by Macrodyne in 1991.At
present, a number of manufacturers offer PMUs as a commercial product, and deployment of PMUs on power
system is being carried out in earnest in many countries around the world. IEEE published a standard in 1991
governing the format of data files created and transmitted by the PMU. A revised version of the standard was
issued in 2005.
Concurrently with the development of PMUs as measurement tools, research was on going on applications of
the measurements provided by the PMUs. It can be said now that finally the technology of synchronized phasor
measurements has come of age and most modern power systems around the world are in the process of
installing wide area measurement systems consisting of the phasor measurement units [2].
4. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3213
Fig. 1 The First Phasor Measurement Units built at Power Systems Research Lab at Virginia Tech
BASIC DESCRIPTION OF PMU
One of the most important features of the PMU technology is that the measurements are time stamped with high
precision at the source, so that the data transmission speed is no longer a critical parameter in making use of this
data. All PMU measurements with the same time stamp are used to infer the state of the power system at the
instant defined by the time stamp. The Global positioning system (GPS) has become the method of choice for
providing the time tags to the PMU measurements. Remember that PMUs evolved out of the development of
the “symmetrical component distance relay”. As shown in fig 2 Analog voltage and current signal obtained
from the secondary windings of the current and voltage transformers. All three phase current and voltages are
used so that positive sequence measurement can be carried out.
Fig. 2: Fundamental Block diagram of PMU
The current and voltage signals are converted to voltages with appropriate shunts or instrument transformers
(typically within the range of ± 10 volts)
So that they matched with the requirements of the analog –to- digital converters. The sampling rate chosen for
the sampling process dictates the frequency response of the anti-aliasing filters. In most cases these are analog-
5. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3214
type filters with cutoff frequency less than half the sampling frequency in order to satisfy Nyquist criterion. The
sampling clock is phase-locked with the GPS clock pulse. Sampling rates have been going up steadily over the
years – starting with the rate of 12 samples per cycle of the nominal power frequency in the first PMUs to as
high as 96 or 128 samples per cycle. The microprocessor calculates positive sequence estimates of all the
current signal, voltage signal, frequency and rate of change of frequency. The time stamp is created from two
of the signals derived from the GPS receiver [2]. PMUs evolved out of the development of the “symmetrical
component distance relay” [4], [6]. The analog inputs are currents and voltages obtained from the secondary
windings of the current and voltage transformers. All three phase currents and voltages are used so that positive-
sequence measurement can be carried out. In contrast to a relay, a PMU may have currents in several feeders
originating in the substation and voltages belonging to various buses in the substation. The current and voltage
signals are converted to voltages with appropriate shunts or instrument transformers (M1) (typically within the
range of ±10 volts) so that they are matched with the requirements of the analog-to-digital converters (M3). The
anti-aliasing filter is present to filter unnecessary disturbance and noise (M2). These three modules constitutes
the Data Collection Module (M123=MD).
In GPS Module (M4), a crystal oscillator is used to supply the sampling clock pulses for the Analog /Digital
(A/D) converting module and track the Pulse Per Second (PPS) supplied by the GPS receiver to correct the error
between PPS and crystal oscillator frequency.
The CPU Module (M5) calculates positive-sequence estimates of all the current and voltage signals and stamps
it with coordinated Universal Time (UTC) supplied by the GPS Module (M4). From the CPU Module (M5) data
are sent data concentrator (PDC) to Super Data Concentrator (SDC) to control centre through Communication
Module (M6). Power Supply Module (M7) supplies power to the PMU. This is illustrated in Fig. 3.
CT/PT
Module
Communication
Module
Anti-aliasing
Filtering
Module
A/D
Converting
Module
CPU
Module
GPS Module
Power Supply
Module
+ -
PDC
M4
M1 M2 M3
M5
M6
M7
Voltage
Signal
Current
Signal
Data collection Module
Fig. 3 Modules of Phasor Measurement Unit
6. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3215
MARKOV MODEL
A Markov chain is a mathematical model for stochastic systems whose states, discrete or continuous, are
governed by a transitional probability. In order for the basic Markov approach to be applicable, the behavior of
the system must be characterized by a lack of memory, that is, the future states of a system are independent of
all past states except the immediately the preceding one. Therefore the future random behavior of a system only
depends on where it is at present, not on where it has been in the past or how it arrived at its present position. In
addition, the process must be stationary, sometimes called homogeneous, for the approach to be applicable. This
means the behavior of the system must be the same at all points of time irrespective of the point of time being
considered, i.e., the probability of making a transition from one given state to another is the same (stationary) at
all times in the past and future. It is evident from these two aspects, lack of memory and being stationary, that
the Markov approach is applicable to those systems whose behavior can be described by a probability
distribution that is characterized by a constant hazard rate, i.e., poisson and exponential distribution, since only
if the hazard rate is constant does the probability of making a transition between two states remain constant at
all points of time
Markov approach can be used for a wide range of reliability problems including systems that are either non-
repairable or repairable and are either series-connected, parallel redundant or standby redundant [7] .Markov
Property: The state of the system at time t+1 depends only on the state of the system at time t
HIDDEN MARKOV MODEL
A hidden Markov model (HMM) is a Markov model in which the system being modeled is assumed to be
a Markov process with unobserved (hidden) states.
Fig. 4: Hidden Markov Model
In this, reliability analysis of PMU is done by Hidden Markov Model (HMM).
Hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a
Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the
observable parameters. The extracted model parameters can then be used to perform further analysis, for
example for pattern/speech recognition applications. In a hidden Markov model, the states are not directly
visible, but variables influenced by the state are visible. Each state has a probability distribution over the
hidden
Markov
model
Hidden
markov
model
7. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3216
possible output tokens. Also, the state transitions are probabilistic in nature. Therefore, the sequence of tokens
generated by an HMM gives some information about the sequence of states. The complete HMM model is
denoted as λ = (A,B,π ). Simple Markov model is the deterministic model where as HMM is the probabilistic
function of the markov chains .This model has been previously used for many applications such as speech
recognition [8,9] bank note recognition etc. and has essentially given fruitful results. Using this method
complexity of calculation is reduced. Using earlier methods we use to calculate only steady state probabilities
but using this method we can calculate the probabilities at every instance .This method has rich mathematical
structure and give more accurate and optimum results than previous methods.
HMM Notation
T=the length of the observation sequence
N=the number of states in the model
M=the number of observation symbols
Q=the states of the Markov process
A=the state transition probability matrix
B=Observation probability Matrix
π=initial state probability Matrix
O=Observation sequence
A. Characterization of an HMM
N, the number of states in the model .These states are hidden ,we denote the individual states as HS={HS1,
HS2,…….., HSN }
The state transition probability matrix A={ aij }
where, aij =P{St+1= HSj | St= HSi}
i.e. It represents probability of reaching state HSj at time t+1 being in state HSi at time t. State transition
depends only on origin and destination(Markov Property)
The observation probability matrix in state
The matrix B = {bj(k)} is N x M,
Where bj(k)=P{observation k at t |state HSj at St} 1 ≤ j ≤ M
The initial state probability π ={ πi },
Where πi = P[S1=HSi],
it represents the probability of being in state HSi at time t=1
HMM has three parameters A,B and π .i.e . it is mathematically represented as
( , , )A B
8. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3217
Hidden Markov Model Problems
HMM is used to solve three problems
Problem 1: Given a model λ = (A,B,π) and observation sequence O, find P(O|λ) Here we want to determine the
likelihood of the observation sequence O.
Problem 2: Given λ = (A,B,π) and O, find an optimal state sequence for the underlying Markov process.
Problem 3: Given an observation sequence O and N and M, finds the re-estimated model λ = (A,B,π)
CALCULATION OF PROBABILITY OF AN OBSERVATION SEQUENCE FOR A GIVEN MODEL:
P(O/λ)
A) Forward Algorithm
Given model λ = (A, B, π) and observation sequence
O=(O1,O2,…,ON), find P(O|λ)
Forward variable at an instant t for a state i is given by
αt(i)= P(O1O2……Ot,St=HSi/ λ)
Where
α = forward variable
π=starting probability matrix
bi(Ot)=observation probability matrix
A= aij=state transition probability matrix
STEP 1 α1 (i) =πibi(O1) for i = 1,…,N
for t = 1,2,…,T and i=1,…,N
STEP 2 αt (i) = tiij
m
j
Obaj1
1-t
for i = 1,…,N
STEP 3 P (O|λ) = i
N
i
1
T
B) Backward Algorithm:
Backward variable at an instant t for a given state i is given by
βt (i)= P(Ot+1Ot+2……OT St= HSi , λ)
Above Equation represents probability of being in state HSi at an instant t and observing all the observations
from the next instant to the end.
9. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3218
β= Calculation of backward variable
π=starting probability matrix
bi(Ot)=observation probability matrix
A= aij=state transition probability matrix
For t = 1,…,T and i=1,…,N
N=4, M=4
STEP 1 Let βT(i) = 1 for i = 1,…,N
for t = T-1,T-2, …,1 and i=1,…,N,
STEP 2 βt(i) =
M
j
ttjij jOba
1
11
C) Determination of Gama
α=forward variable
β=backward variable
P(O|λ)= probability of observation sequence
For t = 1,… T and i=1,…N
N=4,
T=2
OP
ii
i tt
t
*
FOUR STATE HIDDEN MARKOV MODEL: AN ILLUSTRATION
B. The probability for a given observation sequence and the possible state sequence satisfying the observation
sequence are given as follows
Given observation sequence (ON OFF)
The possible state sequences are:
a) HS1-HS4
b) HS2-HS4
c) HS3-HS4
Probability of a given observation sequence ON OFF is P(ON,OFF) and is calculated as follows:
Probability of the state sequence HS1 – HS4 is
10. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3219
P(HS1 H S4)=π1b1(o1)a14b4(o2)
Probability of the state sequence HS2 – HS4 is
P(HS2 HS4)=π2b2(o1)a24b4(o2)
Probability of the state sequence HS3 – HS4 is
P(HS3 HS4)=π3b3(o1)a34b4(o2)
Given Observation sequence is OFF ON
Probability of given observation sequence (OFF ON) is P (OFF,ON)
The possible state sequences are
a) HS4-HS2
b) HS4-HS1
c) HS4-HS3
Probability of a given observation sequence OFF ON is P(OFF,ON) and is calculated as follows:
Probability of the state sequence HS4 – HS2 is
P(HS4 HS2)=π4b4(o2)a42b2(o1)
Probability of the state sequence HS4 – HS1 is
P(HS4 HS1)=π4b4(o2)a41b1(o1)
Probability of the state sequence HS4 – HS3 is
P(HS4 HS3)=π4b4(o2)a43b3(o1)
Given Observation sequence is ON ON
The possible state sequences are
a) HS1-HS1
b) HS1-HS2
c) HS1-HS3
d) HS2-HS2
e) HS2-HS3
f) HS2-HS1
g) HS3-HS3
f) HS3-HS1
h) HS3-HS2
11. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3220
Probability of a given observation sequence ON ON is P(ON,ON) and is calculated as follows:
Probability of the state sequence HS1 – HS1 is
P(HS1 HS1)=π1b1(o1)a11b1(o1)
Probability of the state sequence HS1 – HS2 is
P(HS1 HS2)=π1b1(o1)a12b2(o1)
Probability of the state sequence HS1 – HS3 is
P(HS1 HS3)=π1b1(o1)a13b3(o1)
Probability of the state sequence HS2 – HS2 is
P(HS2 HS2)=π2b2(o1)a22b2(o1)
Probability of the state sequence HS2 – HS3 is
P(HS2 HS3)=π2b2(o1)a23b3(o1)
Probability of the state sequence HS2 – HS1 is
P(HS2 HS1)=π2b2(o1)a21b1(o1)
Probability of the state sequence HS3 – HS3 is
P(HS3 HS3)=π3b3(o1)a33b3(o1)
Probability of the state sequence HS3 – HS1 is
P(HS3 HS1)=π3b3(o1)a31b1(o1)
Probability of the state sequence HS3 – HS2 is
P (HS3 HS2)=π3b3(o1)a32b2(o1)
Given Observation sequence is OFF OFF
The possible state sequences are
HS4-HS4
Probability of a given observation sequence OFF OFF is P(OFF,OFF) and is calculated as follows:
Probability of the state sequence HS4 – HS4 is
P(HS4 HS4)=π4b4(o2)a44b4(o2)
For a given observation O we will get the most probable states that our system can reach.
C. Train the Model Parameters λ= (A, B, ) to Maximize P(O/λ) using Baum-Welch Algorithm
π=starting probability matrix
B=bi(Ot)=observation probability matrix
A= aij=state transition probability matrix
12. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3221
α=forward variable
β=backward variable
P(O|λ)= probability of observation sequence
γt (i)=gamma
γt(i,j)=di-gamma
For t=1 to T-1, and for i=1 to N so for j = 1 to N the di-gammas can be written in terms of α, β A and B as
OP
jObai
ji
ttjijt
t
11
,
The γt(i) and γt(i,j) are related so gamma can be represented
N
j
tt jii
1
,
Given The γt(i) and γt(i,j) we verify below that the model λ = (A,B,π) can be re-estimated as follows
STEP 1 For i = 1,…,N let
ii 1
STEP 2 For i = 1,…,N and j = 1,…,N
1
1
1
1
,
T
t
t
T
t
t
ij
i
ji
aA
STEP3 For j = 1,…, N and k = 1,…, M
1
1
1
1
T
t
t
T
kot
t
t
j
j
j
kbB
Table 1: Reliability Parameters of Basic Components on PMU
MODULE 1 MODULE 2 MODULE 3 MODULE 4 MODULE 5 MODULE 6 MODULE 7
λ1=0.4155 λ1=0.1923 λ1=0.1383 λ1=0.0188 λ1=0.2368 λ1=0.0228 λ1=0.2751
λ2=0.4155 λ2=0.1923 λ2=0.1383 λ2=0.7727 λ2=0.0657 λ2=0.0228 λ2=0.2751
µ1=673.85 µ1= 547.3 µ1 =438 µ1=312.88 µ1=365 µ1=17520 µ1=365
µ2=673.85 µ2= 547.3 µ2 =438 µ2 =365 µ2 =1460 µ2=17520 µ2=365
13. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3222
λ = failure rate μ = repair rate.
Hidden Markov Model for a Two Component System
λ1
µ1
λ2 µ2 λ2 µ2
λ1
µ1
Fig 5 shows Hidden Markov Model for a Two Component System with 4 states
The concept of obtaining Graph from Markov model is well explained in[8].As we can see from fig 5 State 1
represents the working or healthy state of the system(i.e. both the components of the system are working) if any
one of the components stop working but the system is still in working condition then our system reaches State 2
or State 3. When both the components of the system stops working our system comes to State 4 which represent
the non working or failure state of the system. These states of a simple four state Markov model are hidden in
case of HMM.
Fig. 6 below shows the four state hidden Markov model of each state have two distinct observation Module 1 ie
(CT and PT MODULE) .each state has two distinct observation such as ON OFF.
S-1
Comp 1 up
Comp 2 up
S-2
Comp 1 Down
Comp 2 up
S-3
Comp 1 up
Comp 2 Down
S-4
Comp 1 Down
Comp 2 Down
14. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3223
MODULE -1
λ1
µ1
λ2 µ2 λ2 µ2
λ1
µ1
Fig 6 shows the four state hidden Markov model of each state have two distinct observation
The graph of HMM for a two-component system is shownin Fig.6 The concept of obtaining a graph from a
Markov model is well explained in . It is shown in Fig. 6 that state 1 (S1) represents the healthy state of the
system when both the components of the system are working. If any one of the components fails to work while
the rest of the system is still functional, the system transitions to either state 2 (S2) or state 3 (S3). When both
the Components of the system fail to work, then the system goes to state 4 (S4), which represents a failure state
of the system. These states of a simple four-state Markov model are hidden in the case of an HMM, as shown in
Fig. 6. According to the observation states or visible states, The hidden states of the system are predicted.
In the HMM, The hidden states are not visible, but they are probabilistically dependent on each other. The
visible observation states are independent of each other, but they are probabilistic functions of the hidden states.
Therefore, using the HMM based on the current observation, the future observation and state of the system can
be predicted. Eventually, the availability or unavailability of the system at every instance can be computed
using HMM.
The HMM shown in Fig. 6 has four hidden states (HS1, HS2, HS3, HS4) and two observation states (O1,O2)
per hidden state. HMM can be mathematically represented as
( , , )A B
Given λ1= 0.4155
λ2= 0.4155
HS-1
Comp 1 up
Comp 2 up
HS-2
Comp 1 Down
Comp 2 up
HS-3
Comp 1 up
Comp 2 Down
HS-4
Comp 1 Down
Comp 2 Down
ON
OFF
ON
OFF
ON
OFF
ON
OFF
15. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3224
µ1=673.85
µ2=673.85 Δt =0.00002
so λ1=0.4155*0.00002 = 8.3100e-006
λ2=0.4155*0.00002 = 8.3100e-006
µ1=673.85*0.00002=0.0135
µ2=673.85*0.00002=0.0135
λ = failure rate μ = repair rate.
State transition matrix A=
a11=1-(λ1+λ2) = 1.0000
A=
We have initially assumed the values of the B Matrix and PI Matrix
B = and PI =
MODULE -2
Similarly for Module 2 there are four hidden states (HS1, HS2, HS3, HS4) and two observation states (O1 O2) per
hidden state. HMM can be mathematically represented as
( , , )A B
Given λ1= 0.1923
λ2= 0.1923
µ1=547.5
µ 2=547.5 Δt=0.00002
16. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3225
So λ1=0.1923* 0.00002 = 3.8460e-006
λ2=0.1923* 0.00002 = 3.84600e-006
µ1=547.5*0.00002=0.0109
µ 2=547.5*0.00002=0.0109
λ = failure rate μ = repair rate.
a11=1-(λ1+λ2) = 1.0000
State transition matrix A=
A=
We have initially assumed the values of the B Matrix and PI Matrix
B = and PI
MODULE -3
Similarly for Module-3, there are four hidden states (HS1, HS2, HS3, HS4) and two observation states (O1 O2)
per hidden state. HMM can be mathematically represented as
( , , )A B
Given λ1= 0.1383
λ2= 0.1383
µ1=438
µ 2=438 Δt=0.00002
so λ1=0.1383*0.00002 = 2.7660e-006
λ2=0.1383*0.00002 = 2.7660e-006
µ1=438*0.00002=0.0088
µ 2=438*0.00002=0.0088
17. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3226
λ = failure rate μ = repair rate.
a11=1-(λ1+λ2) = 1.0000
State transition matrix A=
A=
We have initially assumed the values of the B Matrix and PI Matrix
B = and PI =
Problem 1: Given a model λ = (A,B,π) and observation sequence O, find P(O|λ). The probability of given
observation sequence is obtained for the individual modules of PMU and PMU as a whole using forward
algorithm.
Results of Problem 1
MODULE 1
Observation sequence = [1,1]
Forward algorithm
STEP 1 α1 (i) =π1bi(O1) for i = 1,…,N
for t = 1,2,…,T and i=1,…,N
Where the sum is from i = 1to N
STEP 1 α1 (i) =π1bi(O1) for i = 1,…,N
α1 (1) =π1b1(O1)= 0.2400
α1 (2) =π2b2(O1) = 0.1200
α1 (3) =π3b3(O1)= 0.2700
α1 (i) =π1b4(O1)= 0.1600
18. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3227
STEP 2 αt (i) = tiij
m
j
Obaj1
1-t
for i = 1,…,N using step-2 we can find α2 (1), α2 (2), α2 (3), α2 (4)
STEP 3
P(O|λ)= 4321 222
1
2
N
i
T i
Alpha matrix for the modules 1, 2, 3, 4, 5, 6 and 7 is as follows:
MODULE-1
alpha matrix( α1)
MODULE- 2
alpha matrix ( α2)
MODULE -3
alpha matrix ( α3)
MODULE 4
alpha matrix ( α4)
MODULE 5
alpha matrix ( α5)
MODULE 6
alpha matrix ( α6)
19. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3228
MODULE 7
alpha matrix( α7)
So find P(O/λ) and Availability of all module using observation sequence.
So unavailability=1-Availability
Availability and Unavailability of components of PMU shown in below table.
Table -2 Availability And Unavailability Of Components Of Pmu For 4 States Hidden Markov Model
Module Availability Unavailability
M1 0.6033 0.3967
M2 0.6065 0.3935
M3 0.6091 0.3909
M4 0.5974 0.4026
M5 0.6116 0.3884
M6 0.8145 0.1855
M7 0.6110 0.389
In Table 2 we can conclude that GPS receiver having low availability compared to other modules therefore
parameter of GPS receiver should be considered properly from design point of view
Problem 2: Given λ = (A,B,π) and O, find an optimal state sequence of each module passes through is found
Results of Problem 2
Parameters (eta & gama Matrix) of a four state hidden Markov model
MODULE 1
Observation sequence = [1,1]
STEP 1 Let βT(i) = 1for i = 1,…,N
for t = T-1,T-2, …,1and i=1,…,N, let
Step 1 βT(i) = 1
β2(1) = 1
20. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3229
β2(2) = 1
β2(3) = 1
β4(4) = 1
STEP 2 βt(i) =
M
j
ttjij jOba
1
11
Using step-2 we can find β1(1), β1(2), β1(3), β1(4)
Beta matrix for the modules 1, 2, 3, 4, 5, 6 and 7 is as follows:
Beta matrix (β1)
Beta matrix (β2)
Beta matrix (β3)
Beta matrix (β4)
Beta matrix (β5)
Beta matrix (β6)
Beta matrix (β7)
α=forward variable and β=backward variable
P(O|λ)= probability of observation sequence
21. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3230
OP
ii
i tt
t
*
t=1
γ1(1) = α1(1)*β1(1) / P(O|λ) i=1
γ1(2) = α1(2)*β1(2) / P(O|λ) i=2
γ1(3) = α1(3)*β1(3) / P(O|λ) i=3
γ1(4) = α1(4)*β1(4) / P(O|λ) i=4
Similarly t=2 we can find γ2(1), γ2(2), γ2(3), γ2(4)gama matrix for the modules 1, 2, 3, 4, 5, 6 and 7 is as
follows:
gama matrix(γ1)
gama matrix(γ2)
gama matrix(γ3)
gama matrix(γ4)
gama matrix(γ5)
gama matrix (γ6)
gama matrix (γ7)
22. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3231
Table -3 Computation of Optimal State Sequence For M1
Observation
Sequence
Possible
States
Sequence
Probability
Value
Optimal State
Sequence
(ON OFF) P(HS1 HS4) 0 P(HS3 HS4)
P(HS2 HS4) 1.9944e-007
P(HS3 HS4) 4.4874e-007
(OFF,ON) P(HS4 HS2) 3.2400e-004 P(HS4 HS3)
P(HS4 HS1) 0
P(HS4 HS3) 4.8600e-004
(OFF OFF) P(HS4 HS4) 0.0078 P(HS4 HS4)
(ON ON) P(HS1 HS1) 0.1920 P(HS3 HS3)
P(HS1 HS2) 1.1966e-006
P(HS1 HS3) 1.7950e-006
P(HS2 HS2) 0.0710
P(HS2 HS3) 0
P(HS2 HS1) 0.0013
P(HS3 HS3) 0.2397
P(HS3 HS1) 0.0029
P(HS3 HS2) 0
From TABLE -3 it can be observed that for an observation sequence ON OFF, the optimal state sequence is
HS3-HS4; for an observation sequence OFF ON ,the optimal state sequence is HS4-HS3; for an observation
sequence OFF OFF, the optimal state sequence is HS4-HS4; for an observation sequence ON ON, the optimal
state sequence is HS3-HS3;
Similarly we can find for M2, M3,M4, M5,M6,M7
Finally, the probability of PMU for different optimal observation sequence is as follows:
PMU
P[ON OFF] = 4.4874e-007 *2.0768e-007*1.4936e-007*3.7090e-007*2.5574e-007*2.4624e-008*2.9711e-
007=9.6595e-048
P[OFF ON] = 4.8600e-004*3.9240e-004*3.1680e-004*3.1680e-004*0.0070*0.0126*2.6280e-004 =4.4364e-
022
P[OFF OFF] = 0.0078*0.0078*0.0079*0.0079*0.0077*0.0024*0.0079=5.5434e-016
P[ON ON] = 0.2397*0.2404*0.2409*0.2415*0.2359*0.1920*0.2412=3.6624e-005
23. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3232
Table -4 Computation Of Optimal State Sequence For PMU
Probable state sequence Optimal state
sequence
(ON OFF) 9.6595e-048
(OFF ON) 4.4364e-022
(OFF OFF) 5.5434e-016
(ON ON) 3.6624e-005
Problem 3: Given an observation sequence O and N and M, finds the re-estimated model λ = (A, B, π) that
maximizes probability of O.
π=starting probabilities matrix
bi(Ot)=observation probabilities matrix
A= aij=state transition probabilities matrix
(γ)- di-gamma
Where sum is from j = 1 to N
Given di-gammas (γ) and gamma (γ)
OP
jObai
ji
ttjijt
t
11
,
N
j
tt jii
1
,
STEP 1 For i = 1,…,N let
ii 1
STEP 2 For i = 1,…,N and j = 1,…,N
1
1
1
1
,
T
t
t
T
t
t
ij
i
ji
aA
STEP 3 For j = 1,…, N and k = 1,…, M
24. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3233
1
1
1
1
T
t
t
T
kot
t
t
j
j
j
kbB
for which Ot = k are counted in numerator.
Results of Problem 3
Re-estimation of the parameters of a four state HMM,
MODULE 1
Observation sequence = [1,1]
gamma
digamma =
A =
B =
PI =
MODULE 2
gamma
digamma =
25. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3234
A =
B =
PI =
MODULE 3
gamma =
digamma =
A =
B =
PI matrix =
MODULE 4
gamma
digamma =
26. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3235
A =
B =
PI =
MODULE 5
gamma
digamma =
A =
B =
PI =
MODULE 6
gamma
digamma =
27. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3236
A =
B =
PI =
MODULE 7
gamma
digamma =
A =
B =
PI =
CONCLUSION
For efficient operation, monitoring and control of the largest and most complex machine such as power system
using wide area measurement system (WAMS), the reliable operation of phasor measurement unit (PMU) is
extremely essential. For such a complex system, Hidden Markov model (HMM) seems to be promising for
computing The probability of given observation sequence is obtained
28. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3237
for the individual modules and PMU as a whole using forward and. Secondly find backward algorithm and the
optimal state sequence each module found. Thirdly the parameters of the hidden Markov model are re-
estimated using Baum-Welch algorithm. it has been observed that the availability of GPS Receiver is lower
compared to other modules thus ,it is considered as the most sensitive component of PMU and should be given
proper attention to it from design point of view for reliable operation of PMU.
REFERENCES
1. Pei Zhang, Fangxing Li and Bhatt, N., “Next-Generation Monitoring, Analysis, and Control for the
Future Smart Control Center” IEEE Trans. Smart Grid., vol. 1, no. 1, pp.186-192, July 2010.
2. A. G. Phadke, “Synchronized Phasor Measurements in Power Systems”, IEEE Comp. Appl. Power
Syst., vol. 6, no. 2, pp.10–15, 1993.
3. Bindeshwar Singh , N.K. Sharma , A.N. Tiwari , K.S. Verma and S.N. Singh, “Applications of Phasor
Measurement Units (PMUs) in Electric Power System Networks Incorporated with FACTS Controllers”
International Journal of Engineering, Science and Technology, vol. 3, no. 3, pp. 64-82, 2011.
4. Yang Wang,Wenyuan Li,Jiping Lu, "Reliability Analysis of Phasor Measurement Unit Using
Hierarchical Markov Modeling", Electrical Power Components and Systems, vol. 37, no. 5, pp.517-532,
April 2009.
5. Farrokh Aminifar, Mahmud Fotuhi," Reliability Modeling of PMUs Using Fuzzy Sets”, IEEE
transaction on Power Delivery, vol.25, no.4, pp 2384 - 2391, October 2010.
6. Peng Zhang, Ka Wing Chang, "Reliability Evaluation of Phasor Measurement Unit Using Monte Carlo
Dynamic Fault Tree Method", IEEE Transaction on Smart Grid, vol. 3, no. 3, September, 2012.
7. Roy Billinton, Ronald N. Allan, “Reliability Evaluation of Engineering Systems”, 2nd Edition,
Springer, US.
8. R. Rabiner, “Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition",
Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, February1989.
9. M. Stamp, “A Revealing Introduction to Hidden Markov Models,” September28,2012
http://www.cs.sjsu.edu/faculty/stamp/RUA/HMM.pdf.
29. Amaresh Choudhury IJSRE Volume 3 Issue 4 April 2015 Page 3238
Amaresh choudhury received the B.tech Eng. from Sanjaya memorial institute of technology, Berhampur odisha, in 2011 and
M.tech Eng.degrees in electrical engineering from National Institute of Science and Technology, Berhampur, odisha in 2014; He was guest Lecturer in
Parala maharaja engineering college from 01.09.2014 to 30.11.2014, Berhampur, Ganjam .His major research interests include wide-area Monitoring
system and its application in power system