Sunday, January 26, 2020

Review of Optimal PMU Placement Methods

Review of Optimal PMU Placement Methods Abstract-The Phasor Measurement Unit (PMU) is very important tool for monitoring and control of the power system. PMUs give real time, synchronized measurements of voltages at the buses and also current phase values which are incident to those buses where these PMUs are located. It is unnecessary and impossible to place PMU at each bus to estimate the states because the PMUs and communication facilities are very costly. It is necessary to determine the minimum number of PMUs for entire observability of the power network. The optimal placement of PMUs (OPP) problem solved by various techniques such as mathematical programming, metaheuristic techniques. A literature review on these technologies to solve OPP problem is proposed in this paper. I. INTRODUCTION At present due to increased power demand, fast growth of generation, transmission, and development in power systems congested the existing networks and therefore stability margin of these networks are decreased. In this situation to make sure proper and stable operation of the power system, an accurate measurement and system states monitoring is required. This was normally done by Supervisory Control and Data Acquisition (SCADA) system, where system states estimation depends on unsynchronized measurement[1]. These measurements have errors such as measurement and telemetry bias. To overcome these limitations in the SCADA, Wide Area Monitoring Protection and Control (WAMPAC) system is used[2]. This system consist Phasor Measurement Units (PMUs) as fundamental components which give synchronized and real-time voltages and currents phasor measurement[3]. Global Positioning System Satellite (GPS) provides reference timing signals to achieve synchronization of sampling voltage and current w aveform with respect to this reference time. A PMU directly measures the voltage Phase of the bus where these PMUs are placed and also measure the current phases of a few or all the branches connected to that bus. In recent years to improve monitoring use of PMUs are rapidly increases, so it needs to place these PMUs on all of the buses for full observability of the network. It is also impossible to place these units on entire system buses because PMUs and communication services are very costly[4]. Thus determination of the optimal number of PMUs and its location for overall observability of the system is very important. A proper methodology is required to find the optimum number of the PMUs which will fully observe the power network. To solve the Optimal PMUs Placement (OPP) problem a number of methods have been employed[5]. These methods usually classified into conventional methods and advanced heuristic and modern metaheuristic methods[6] : Linear Programming, Nonlinear Programming, Dynamic Programming are the common optimization methodologies are proposed to solve this problem. Problems such as difficulties of obtaining local minima and handling constraints in conventional techniques are overcome by advanced heuristic and modern metaheuristic optimization methodology. These methodologies are Depth First Search, Minimum Spanning Tree, Simulated Annealing, Tabu Search, Genetic Algorithms, Differential Evaluation, Immune Algorithms, Partical Swarm Optimization or Ant Colony Optimization [7]. This paper reviews the research work and studies that have been done in the area of optimal placement of phas or measurement units (PMUs). Mainly the conventional and recent advanced heuristic and metaheuristic optimization techniques are presented in this paper to solve the typical optimal placement of PMUs problem. The formulation of this problem is described in Section II.The new methods to solve the OPP problem are discussed in Sections III and IV. Section V concludes this paper. II. OPTIMAL PMU PLACEMENT (OPP) PROBLEM FORMULATION PMU is an intelligent device which measures the phase value of voltage and current of bus which are connected to it. Figure 1 shows PMUs which purely isolated form a Wide Area Monitoring System (WAMS).GPS time stamped measurement signals are fed to a Phasor Data Concentrator (PDC) by using PMUs. The PDC collects and sorts the phasor measurements and signal processor converts data of PMUs into useful information which is visible on Human Machine Interface (HMI).The operator can easily access the critical information of the power system state. Some rules can be used for the placement of PMUs which are given in [8] like, assigning one voltage measurement at the bus where PMU is located, one branch current measurement, one voltage and current pseudo measurement. Figure 1.Layout of PMU along with GPS time stamped signals The PMUs can be placed at planned buses to completely observe the total network. These located PMUs are measuring the voltage phase value of that bus and current phase values of the lines which are connected to the same bus. The aim is to completely observe the network with an optimum number of PMUs. The problem for n-bus system is formulated and solved by Integer Programming method [6]as given below: Min Subject to f(x) Where x = binary decision variable vector, . The nonlinear constraint expressions are created considering the placement and types of available measurements. Assume the phasor value of voltage at the bus where PMU located and values of current phasors along the branches which connected to that bus will be easily accessible. The other adjacent bus voltages will also be accessible. Determine the solution vector which is a set of minimum and satisfy above equation. The constraint function can be defined with the help of Binary Connectivity Matrix A which gives the information about bus connectivity of power network. The elements of matrix A is defined as, = 0 otherwise. The constraint equations are considered for the three cases: (1) PMU measurements only, (2) PMU measurements and injections (i.e. zero injections) and (3) PMU measurements, injections, and flows. Different formulations of the PMU placement problem with additional constraints have been presented in the literature, Effects of Zero Injection Buses[9], Effect of conventional measurements[10], single or multiple PMU loss contingency[11], single branch outage[12], contingency of single line outage or single PMU loss[13], effect of PMU channel limit[14]. III. MATHEMATICAL PROGRAMMING METHODS Integer Programming (IP) is a numerical programming method it also known as mathematical programing. It solves an optimization problem which has integer design variables. According to reference [15], whether they are linear, nonlinear or quadratic, an integer programming is divided into Integer Linear Programming (ILP), Integer Nonlinear Programming (INLP) and Integer Quadratic Programming (IQP) respectively. This paper gives the implementation of Integer Linear Programming (ILP) for optimal PMU placement for full power system observability. Modeling of zero injection constraints in ILP frame work has given. A method has been proposed to the systems having zero injection busses in which we use binary connectivity matrix modification and the modified matrix can be used in Integer Linear programming (ILP) for optimal PMU placement. ILP approach has also been given for the systems considering single PMU outage. The results specify that: 1) optimal PMU placement for full power system observability can be computed effectively; 2) connectivity matrix modification based approach for systems having zero injection buses is computationally efficient and easy to execute; 3) number of PMUs has to increase for systems considering single PMU outage. The proposed algorithms have been tested for IEEE 9 bus, IEEE 14 bus, IEEE 24 bus test systems on MATLAB environment [16]. This paper presents a unified binary semidefinite programming (BSDP) model with binary decision variables, for optimal placement of phasor measurement units, considering the impact of pre-existing conventional and synchronized phasor measurements as well as the limited channel capacity of phasor measurement units. A linear objective function is minimized subject to linear matrix inequality observability constraints. The developed method is solved with an outer approximation scheme based on binary integer linear programming. The proposed method is illustrated using the IEEE 14-bus test system. Simulations are conducted on the IEEE 57-bus and 118-bus test systems to prove the validity of the proposed method [17]. For the observability of system, an Integer Linear Programming (ILP) method is used. It also reduces the number of PMUs and maximizes the measurement redundancy in the power system buses. This paper utilizes two approaches, Newton Raphson method and Weight Least Squares (WLS) state estimation method for estimating voltage magnitude and phase angles at each bus. The true value obtained from NR method is compared with the estimated values obtained from WLS with and without the inclusion of PMU measurements. The employed techniques are tested on IEEE- 14 and 30 bus system for determining the optimal points of placement of PMUs to measure the accurate voltage magnitude and phase angle at each bus [18]. We define the desired solution as the PMU placement that also achieves best overall state estimation performance. Accordingly, we derive the state estimator of all buses in a three-phase network and propose a) greedy algorithm and b) integer programming optimization method to determine the optimal solution. The comparative performance of these two methods is presented via evaluation of transmission and distribution test networks [19]. This paper aims to optimize the PMU (Phasor Measurement Unit) placement for a full observation of the power network and the minimum number of PMUs. In this paper competition of Mixed Integer Non-Linear Programming and heuristically algorithms such as Bacterial Foraging Algorithm was presented. The results are demonstrated with PMU placement optimization simulation and a redundancy measurement analysis by using IEEE14-bus and Tehran Regional electric company 41-bus networks [20]. This paper presents a method for the use of synchronized measurements for complete observability of a power system. The placement of phasor measurement units (PMUs), utilizing time-synchronized measurements of voltage and current phasors, is studied in this paper. An integer quadratic programming approach is used to minimize the total number of PMUs required, and to maximize the measurement redundancy at the power system buses. Existing conventional measurements can also be accommodated in the proposed PMU placement method. Complete observability of the system is ensured under normal operating conditions as well as under the outage of a single transmission line or a single PMU. Simulation results on the IEEE 14-bus, 30-bus, 57-bus, and 118-bus test systems as well as on a 298-bus test system are presented in this paper [21]. B. Exhaustive Search Exhaustive search is a general optimization technique that systematically enumerates all possible candidates for the solution and selects the candidate that satisà ¯Ã‚ ¬Ã‚ es the constraints at the optimum value of the objective function. Its main advantage is that it guarantees the à ¯Ã‚ ¬Ã‚ nding of the global optimum. However, it is not suitable for large-scale systems with huge search space. Observability of bulk power transmission network by means of a minimum number of phasor measurement units (PMUs), with the aid of the network topology, is a great challenge. This paper presents a novel equivalent integer linear programming method (EILPM) for the exhaustive search-based PMU placement. The state estimation implemented based on such a placement is completely linear, thereby eliminating drawbacks of the conventional SCADA-based state estimation. Additional constraints for observability preservation following single PMU or line outages can easily be implemented in the proposed EILPM. Furthermore, the limitation of communication channels is dealt with by translation of nonlinear terms into linear ones. Optimal PMU placement is carried out on the IEEE 118-bus test system in different scenarios. The comparison between obtained results of EILPM and those of other methods reveals optimality of the solutions. Moreover, the proposed method is successfully applied on the Iranian National Grid, which demonstrates it can effectively be employed for practical power networks [22]. This paper gives Exhaustive Search (ES) algorithms for optimal PMU placement for full power system observability. The results specify that: 1) optimal PMU placement for full power system observability can be computed effectively; 2) connectivity matrix modification based approach for systems having zero injection buses is computationally efficient and easy to execute; 3) number of PMUs has to increase for systems considering single PMU outage. The proposed algorithms have been tested for IEEE 9 bus, IEEE 14 bus, IEEE 24 bus test systems onMATLAB environment [16]. This paper presents a unified binary semidefinite programming (BSDP) model with binary decision variables, for optimal placement of phasor measurement units, considering the impact of pre-existing conventional and synchronized phasor measurements as well as the limited channel capacity of phasor measurement units. A linear objective function is minimized subject to linear matrix inequality observability constraints. The developed method is solved with an outer approximation scheme based on binary integer linear programming. The proposed method is illustrated using the IEEE 14-bus test system. Simulations are conducted on the IEEE 57-bus and 118-bus test systems to prove the validity of the proposed method [17]. IV. HEURISTIC ALGORITHMS A. Genetic Algorithm (GA) Genetic algorithm (GA) is adaptive heuristic search algorithm that repeats the process of natural evolution. This process is used to generate solutions to optimization and also search problem, The utilization of Genetic Algorithms (GA) in tackling engineering problems has been a major issue arousing the curiosity of researchers and practitioners in the area of systems and engineering research, operations research and management sciences in the past decades are described in [23]. This paper models genetic algorithm into the Map Reduce model, so the MapReduce genetic algorithm (MRGA) possesses some parallel computing performance, such as scalability, better fitness convergence and so on. MRGA is implemented on computing clusters of Hadoop to search the optimal configuration of PMU. Meanwhile, this feasibility and the computing performance of MRGA is verified by the IEEE14-node system, IEEE118-node system, and Wp2383-node system. This method has significant advantages in the installed PMU number, the diversity of solution, the astringency and the practicability [24]. B. Tabu Search (TS) This paper introduces a recursive Tabu search (RTS) method to solve the OPP problem. More specifically, the traditional Tabu search (TS) metaheuristic algorithm is executed multiple times, while in the initialisation of each TS the best solution found from all previous executions is used. The proposed RTS is found to be the best among three alternative TS initialisation schemes, in regard to the impact on the success rate of the algorithm. A numerical method is proposed for checking network observability, unlike most existing metaheuristic OPP methods, which are based on topological observability methods. The proposed RTS method is tested on the IEEE 14, 30, 57 and 118-bus test systems, on the New England 39-bus test system and on the 2383-bus power system. The obtained results are compared with other reported PMU placement methods. The simulation results show that the proposed RTS method finds the minimum number of PMUs, unlike earlier methods which may find either the same or even higher number of PMUs [25]. The contribution of this paper is as follows: at first, analyze the measurement placement design of the electric power system using the software PSAT. Second, the heuristic approach, Tabu search (TS), based on topological analysis is proposed to solve the problem. The heuristic algorithm uses augmented incidence matrix to focus on the power system state estimator model then an Optimal PMU Placement (OPP) problem is formulated for the configuration with the minimum number of measurements that satisfies the observability constraints. Tests on the IEEE 14-Bus system and the TN are used to demonstrate the validity, flexibility, and efficiency of the proposed approach [26]. C. Simulated Annealing (SA) This paper proposes a two-step optimization approach for optimal placement of phasor measurement unit (PMU) to obtain complete observability of power system in the case of preinstalled PMUs. The complete observability of the system in the case of normal operation and pre-installed PMUs is formulated and then, different contingency conditions in the system are considered, i.e. single line outage and single bus outage. At the first step of the proposed two-step optimization approach, a minimization model is applied to convex programing (cvx) to achieve the minimum number of PMUs which guarantees the complete observability of the system. At the second step, simulated annealing (SA) is applied to maximize the measurement redundancy. Additionally, to further reduce the number of required PMUs the zero-injection bus effect is considered. At last, the proposed approach is tested on several IEEE standard systems, i.e. IEEE 14-bus, 30-bus, 39-bus, IEEE 16-machine 68-bus and 118-bus, to demons trate the effectiveness of the proposed approach [13]. This paper presents a novel Multi-Stage Simulated Annealing algorithm for the joint placement of PMUs along with the existing conventional measurement units in the power grid network. The proposed multi-stage optimization method enables Simulated Annealing to reach the optimal point faster than conventional Simulated Annealing methods. The controlled uphill movements during various stages facilitate to obtain best possible solution [27]. D. Differential Evolution (DE) In this paper, differential evolution (DE) algorithm has been proposed to solve an optimal joint placement problem of phasor measurement units (PMUs) and conventional measurements which enable to determine the state variables of the power system. The problem is to minimize the number of PMUs required for network observability and to maximize the PMU measurements redundancy. This is achieved by selecting a solution with maximum System Observability Redundancy Index (SORI) if multiple optimal solutions exist. The resulting nonlinear integer programming (NLIP) problem is solved by the proposed DE method for the optimal solution by considering different power system problems viz. a 7-bus test and IEEE 14-bus systems with and without the consideration of zero injection buses. Results thus obtained have also been validated with existing solution techniques [28]. E. Particle Swarm Optimization (PSO) An exponential binary particle swarm optimization (EBPSO) algorithm is proposed to solve the OPP problem for a completely observable network. Various practical contingencies such as zero injection, single PMU outage are considered in the proposed algorithm along with the normal operating condition. Multiple solutions for OPP problem can improve the feasibility of the placement methodology in a practical environment. Even though any bus is selected as candidate location but it may not be possible to install a PMU on that bus due to the lack of necessary infrastructure. On the contrary, few buses in practical systems which require close and precise monitoring should be directly observed by PMU. Placing some extra PMUs can solve this problem but economically it is not preferable. Hence, having alternative solutions can be very effective. To ensure multiple solutions and improve the performances, an adaptive exponentially decaying inertia weight coefficient is developed. A sigmoid functi on is introduced to update the position of the particles in binary form. Both inter connected (IEEE 14-bus and 30-bus) and radial (IEEE 39-bus) system are tested to check the feasibility and effectiveness of the algorithm [29]. This paper proposes a Particle Swarm Optimization based method to find the optimal PMU locations in a given grid topology. This method was tested successfully with the IEEE 14-bus, 30-bus, and 68-bus systems as well as with a large portion of the Brazilian power system [30]. This paper presents an Improved PSO Algorithm (IPSO) to solve the problem of optimal Phasor Measurement Unit (PMU) placement. The aim of Optimal PMU Placement problem is to guarantee both full observabilities of the power grid and minimal number of PMU. In the Improved PSO Algorithm, the point of genetic algorithm and the simulated annealing process is involved into basic particle swarm optimization. To deal with the constraints, an improved Algorithm is developed and it can avoid costing much time and trapping local optimal solution. IEEE systems are tested to show the feasibility and effectiveness of the algorithm [31]. F. Immune Algorithm (IA) G. Iterated Local Search (ILS) The objective of the paper is to minimise the size of the PMU configuration while allowing full observability of the network. The method proposed initially suggests a PMU distribution which makes the network observable. The Iterated Local Search (ILS) metaheuristic is then used to minimise the size of the PMU configuration needed to observe the network. The algorithm is tested on IEEE test networks with 14, 57 and 118 nodes and compared to the results obtained in previous publications [32]. H. Spanning Tree Search The objective is to use the spanning tree approach and tree search technique for optimal placement of multichannel and minimum channel synchronized phasor measurement units (PMUs) in order to have full observability of Power System. The novel concept of depth of observability is used and its impact on the number of PMU placements is explained. The spanning tree approach is used for the power system graphs and a tree search technique is used for finding the optimal location of PMUs. This is tested on IEEE-14 and IEEE-30 bus system. The same technique is modified to optimally place minimum channel PMUs on the same IEEE-14 and IEEE-30 bus systems. Matlab tool has been used for fulfilling the objective [33]. I. Greedy Algorithm Paper [34] propose a greedy PMU placement algorithm and show that it achieves an approximation ratio of (1-1/e) for any PMU placement budget. We further show that the performance is the best that one can achieve, in the sense that it is NP-hard to achieve any approximation ratio beyond (1-1/e). Such performance guarantee makes the greedy algorithm very attractive in the practical scenario of multi-stage installations for utilities with limited budgets. Finally, simulation results demonstrate the near-optimal performance of the proposed PMU placement algorithm. This paper studies the placement problem of PMUs in distribution system considering the system reconfiguration. System reconfiguration is achieved using the ant colony optimization method to solve the minimum power losses problem. A Greedy algorithm is used as an optimization tool to determine the minimal number of PMUs and their locations. The 33-bus distribution system is studied for optimal installation of PMUs with different distribution network topologies [35]. J. Recursive Security Algorithm The recursive security algorithm is a spanning tree search of multiple solutions, with a different starting point. Recursive spanning tree algorithm of PSAT is applied to find out the minimal placement locations for observability of all buses. The Thevenins equivalent parameters have been obtained from the measured and estimated voltages at the load buses and impedance matrix Zbus. The parameters obtained are used to find the voltage stability boundary. Results on the IEEE-14 bus system and IEEE-30 bus system are presented to illustrate the proposed approach [36]. K. Teaching-Learning-Based optimization Algorithm In this paper, Teaching-Learning-Based optimization Algorithm (TLBO) is presented for solving the problem of placement of PMU optimally in a power system network for complete observability. The TLBO algorithm enables optimal PMU placement by zero injection measurements and also by not including zero injection measurements. The algorithm has been tested on standard test systems such as IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and the results are contrasted with other optimization algorithms like Genetic Algorithm and Binary PSO [37]. L. Improved binary particle swarm This paper presents the improved binary particle swarm (IBPSO) method that converges faster and also manage to maximize the measurement redundancy compared to the existing BPSO method. This method is applied to IEEE-30 bus system for the case of considering zero-injection bus and its effectiveness is verified by the simulation results done by using MATLAB software [38]. M. Best first search (BFS) algorithm This paper utilizes best first search (BFS) algorithm to determine the optimal placement of PMUs for complete observability of a power system under normal operating conditions. The additional redundancy offered by this method has been removed by applying a pruning technique to further minimize the number of PMUs determined by BFS algorithm. The proposed method has been used to determine the optimal PMU placement solutions for the standard IEEE 14-bus system, IEEE 30-bus system and a practical 246-bus Indian system. The results obtained with the proposed method have been compared with the existing methods such as integer linear programming. It has been found that the proposed method is able to achieve the complete system observability with the minimum number of PMUs required [39]. N. Mixed heuristic/matheuristic method This paper presents a new method for the optimal allocation of PMUs in substations with a focus on the two-level state estimation process that was recently proposed in the specialized literature. A mixed heuristic/matheuristic method is proposed to determine the number and location of those units in such a way to provide robust observability characteristics. Its reliable, robust, and precise results are shown for small and large substation layouts [40]. O. Measurement sensitivity analysis This article presents a novel algorithm to find optimal sets of Phasor Measurement Units (PMUs) in power systems using measurement sensitivity analysis aiming for fault detection without multi-estimation. The algorithm generalizes the impedance method in fault detection through optimizing PMU utilization in order to detect a fault with desired precision in interconnected power systems. By deriving bus voltage and currents sensitivity indices to the fault location and impedance, possible deviations of the estimated fault location and/or impedance due to measurement noise, accuracy, precision limits, or simply the inability of a measurement point to sense a fault is evaluated. Therefore, the algorithm can solve Optimal PMU Placement (OPP) for desired fault detection precision based on these indices for various points of measurement observing faults in the system. Finally, avoiding multi-estimation guarantees the unique mapping between measurements of the selected PMU sets and faults th roughout the system. The proposed algorithm is performed on the IEEE 7-bus and 14-bus benchmark systems and the fault location capability is evaluated through neural networks [41]. P. Modified binary cuckoo optimization algorithm In this study, a new evolutionary algorithm named as modified binary cuckoo optimization algorithm (MBCOA) is presented to solve optimal PMU placement (OPP) problem. The proposed method is classified as topological approaches. The basis of the method is in the lifestyle of the brood parasite bird named cuckoo that immigrates to the best habitat to obtain sufficient food and suitable nests for egg laying. The proposed binary structure is not introduced and applied to OPP problem up to now. OPP is tested on different networks consist of IEEE 14, 30, 57 and 118 bus test systems during normal operation and single event contingencies, i.e. single PMU failure and single line outage. The proposed MBCOA is also applied to 2383 and 2746 bus test systems to show its ability to handle large scale power networks. It is shown that MBCOA can obtain the best result from the search region with a minimum number of iterations [42]. References: [1]M. A. Rahman, A. H. M. Jakaria, and E. Al-shaer, Formal Analysis for Dependable Supervisory Control and Data Acquisition in Smart Grids, in 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2016, pp. 263-274. [2]Jiaping Liao and Cheng He, Wide-area monitoring protection and control of future power system networks, in 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), 2014, pp. 903-905. [3]M. Wache, Application of phasor measurement units in distribution networks, in 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013), 2013, pp. 0498-0498. [4]A. Pal, A. K. S. Vullikanti, and S. S. Ravi, A PMU Placement Scheme Considering Realistic Costs and Modern Trends in Relaying, IEEE Trans. Power Syst., pp. 1-1, 2016. [5]J. Paudel, Xufeng Xu, and E. B. Makram, PMU deployment approach for maximum observability considering its potential loss, in 2016 IEEE/PES Transmission and Distribution Conference and Exposition (TD), 2016, pp. 1-5. [6]K. K. More and H. T. Jadhav, A literature review on optimal placement of phasor measurement units, 2013 Int. Conf. Power, Energy Control, pp. 220-224, Feb. 2013. [7]N. M. Manousakis, G. N. Korres, and P. S. Georgilakis, Optimal placement of phasor measurement units: A literature review, in 2011 16th International Conference on Intelligent System Applications to Power Systems, 2011, pp. 1-6. [8]V. V. R. Raju and S. V. J. Kumar, An optimal PMU placement method for power system observability, in 2016 IEEE Power and Energy Conference at Illinois (PECI), 2016, pp. 1-5. [9]K. Gharani Khajeh, E. Bashar, A. Mahboub Rad, and G. B. Gharehpetian, Integ

Saturday, January 18, 2020

Generalization: Black People and Young Men Essay

In daily life, you can find out man many generalization easily; such as, when you heard about crime, you immediately think of the drunken, unemployed, color people..etc, or when you heard about Havard’s student, words describing like very smart, creative, sucess in life easily or something like that glance through your mind.In the same way, when you heard people depend on welfare, you immediately think that they are lazy, unemployed, have many children, never try to get any job and they are black people. However, do you think generalizations like above always right? Absolutely not. In my opinion, each person has each generalization, sometimes genelizations is similiar, but sometimes not. Wrong genelizations can be cause racism or unfair in life. To begin with an Gladwell’s article, because he mention generalization in one of his articles. In â€Å"Troublemakers†, Gladwell argues that generalization is not easy, you must know exactly what is going on. Because of the attacks of pitt bull, it was banned by the Ontario goverment. They said that pitt bulls is very dangerous, can bite someone without warning signs, then, they decided banned it. In the same case with pitt bull, he gave us some example about young men driver always higher charges or doctors think that midle-ages easily get heart attack. In the same article, he also gave us example about terrorism. He said terrorist in our mind is totally different in real. We don’t know how a terrorists look like; can be a Arab men, can be a young men, can be black people, can be white people, can be lady, also can be a old men. That’s why, the goverment and the police, specific is New York Police Department have trouble in sketching terrorist’s prolife. However, when NYPD use right generalization to make decrease crime in city. Back to the pitt bull, as we know, not all pitt bull are killer, moreover, dogs are good or bad also depend on owner. Most of case was attacked by pitt bull, the owner is often neglectful. However, it is still prohibited. Therefore, Gladwell said that is wrong generalization. As shown above, generalization is not always right. In a welfare statistics 2012, total government spending on welfare annually (not including food stamps or unemployment): $131.9 billion. This number is increasing every year. Besides, the global economics downturn, this number is very serious. That’s why, most of americans don’t like people who depend on welfare too much. Americans thought that it was one of cause badly affecting in life today. In the same statistic was shown above, percent of recipients who are black: 39.8 %, this’s highest percent. Next, base on those numbers, you are thinking that black people depend on goverment so much and they seem never try to getting job. Moreover, most of drunken and crime you can see everyday on the street are also black. In the same case, when you go to department of welfare center, you can easily realize that the number of black people is more than all. Now, you begin to default on your thinking that black people are lazy, drunken, crime and depend on welfare so much. Meanwhile, how we know they never try to find a job or try to do something? We don’t know. We also don’t know what is going on their life. They can really have more troubles than white, because of the racism. I have a small story, that’s observation. I’m living Northeast Phillies. Oneday, when I was from home to school, I saw some homeless man on the way to school, I counted five people and in which just have one black people. Now, where is problem? I know, this’s just small story and it can’t generalize anything obvious. However, I think it enough for we look back at own conclusion. Fact that blacks depend on welfare more than others, don’t they? When we heard about black people, we immediately think of crime, drunken†¦ And, thinking made us don’t want to hire them. Clearly, they’ll unemployed. Next, they must depend on welfare to maintain life. Things like a circle and has no end. Until we change our thinking about them and giving them more opportunities, they are still depend on welfare. Not all black people depend on welfare, also not all pitt bull are killers. Finally, genaralization is really important and need shrewdness. You must observation everything what is happening around problems. Because a wrong genaralization can can lead to unnecessary mistakes.

Friday, January 10, 2020

Corporate University in China Essay

The concept of corporate university (CU) in China is a recent phenomenon although it existed more than eight decades in the western world. Literature reviews indicate that CU is an independent professional-managed entity proactively providing learning intervention in the workplace. With the ownership of the corporation, CU embedded culture and optimized learning through commitment to strategic intent in order to meet organizational objectives. The concept of â€Å"training† has to be redefined. The major key role of CU is to facilitate both individuals and organization to become â€Å"efficient learner† in order to maintain competitiveness in the ever-changing of business environment. Since the start of economic reform in 1978, the Chinese economy has enjoyed a dramatic growth. In 2002 alone, China attracted over US$52.7 billion in foreign direct investment (FDI), surpassing the US. The drastic economic growth and the fundamental structural change in China as a result of government policies, globalization and technological advances will continue to drive the demand for training and competency development. Both local and foreign-invested corporations seek the CU concept as the strategic solution. There are many reasons for corporations establish CU; however, the primary one is to facilitate corporate objectives and support business strategies. Studies on HRM suggest different models vary across different countries. A direct copy from western model might cause ineffective and inefficient. A comprehensive understanding on the CU meaning, how it operates, and its roles are important. In addition, consideration of local elements is necessary in adopting CU in China. Major Chinese characteristics with current corporate situation and issues should be identified. Evidences support that the Chinese contextual variables and their CU motives impact the strategies and development of CU in China. Adapted from Prince & Beaver’s conceptual CU Wheel model, a priori China CU framework encompassed the unique characteristics of China is formulated in an attempt to describe the key functions that an â€Å"ideal† type of CU in China should perform. Based on the theoretical assumptions, the four core subsystems include learning and teaching process, networks and partnership, accreditation system, and marketing process constitute the main elements of the CU process. They work collaboratively with the common goal of supporting business goals and strategies. The two cultural elements, â€Å"guanxi† and â€Å"mianzi†, work as catalyst or lubricator to enhance the effectiveness and coordination. The priori CU framework brings new insight to the CU development in China. Research is performed to test the relevance of this framework across the three major forms of ownership. To achieve the research objectives, an exploratory and descriptive approach is used. This study adopts a qualitative case-methodology based on the in-depth interviews, previously collected data through questionnaires and documentary analysis. The three study cases include: 1. Taikang Business University (TBU) – a joint-venture enterprise with foreign investment; 2. Motorola University, China (MUC) – a wholly foreign-owned multi-national corporation (MNC); 3. X Academy, a state-owned enterprise (SOE) To delimit the study, each case covers background information, its objectives and roles, core portfolio, and the relevance of the priori framework. Challenges and issues of each case organization are addressed as well. A cross-case analysis of the three case organizations is used to identify the similarities and differences. The diffusion of CU practices varies with the investment form and the foreign equity stake. The study provides evident that both the MNC and joint venture case organizations with foreign investment are more mature in their CU process. The integration and coordination of the four core processes that constitute the priori CU framework are strongly evident. A hybrid model of CU practices is adopted with the convergence of practices from the parent country operation and the divergence of practices for the China context. On the other hand, the CU development of the SOE case is less developed. It can be explained that most of SOEs in China lack western management know-how and resources. Some CU sub-processes or practices of the priori CU framework are either missing or too weak in the SOE case. It has been facing problems at the CU evolution. Misconception on training, lack of support from upper management, lack of CU understanding, unable to demonstrate the CU value, ineffective learning process, and the cultural gap are the major issues and challenges for CUs in China. Improvements have to be made before CU can really take off in organizations in China. Recommendations with reference to the priori CU framework are made. Additional comments on CU strategies are given for local enterprises and the foreign-invested organization. It is evident that some CUs such as Motorola University China (MUC) perform a range of strategic functions in China. Among all, it can be summarized into two major categories: developing people and developing business. Although developing people is the most common motive for the CU establishment, the CU strategic orientation towards market-driven and profit-driven is more evident and justifiable to most Chinese enterprises. The CU strategies and practices are highly influenced by the political, economic and cultural characteristics of China. It would be difficult to apply a single CU model to all CU phenomena. Despite the limitation, the priori CU framework can still be used as a tool to describe the current situation in the CU scene in China. It encompasses the unique characteristics of China CU, capable of providing the direction to the CU operations and practices. This research raises a number of issues upon which subsequent research efforts can be expended as follow: 1. Besides the forms of ownership, other company variables such as the industry types, leadership style, corporation sizes, and corporate culture might affect the CU development and practices. Further, the CU strategies and practices are highly influenced by the political, economic and cultural characteristics of China. To what extent these contextual variables influence the CU adoption in China? 2. The major motive for local enterprises to establish CU is to drive corporate-wide initiative, reinforcing and perpetuating behavior towards internationalization. So how do the local corporations, particularly state-owned enterprise, change their traditional view to more global perspective in order to run a successful CU? 3. Different foreign-invested corporations adopt different local strategy. Some focus on globalizing the China operation whilst others prefer to adopt a complete localization approach. Does the local strategy adopted by the foreign-invested corporation affect the CU strategy and development? 4. The dynamic business environment in China creates a constant change phenomenon in corporate strategies. How does CU support the changing corporate strategies and maintain its agility? How does CU demonstrate its value in China? With a large population, fast and rapid growing economy and constant improvement of its people’s living standard, corporations in China enjoy tremendous market potentials. CU definitely has an important role in China. However, a successful CU requires continuously learning and self-reflective. The evolution of CU involves ongoing values, trust, respect, commitment, integrity and enthusiasm. The priori CU framework, to a certain extent, can be used as a tool to explain the current situation in the CU scene in China. With more understanding on the CU practices and development in China, it will benefit both organization decision makers and educational providers to evaluate their responses to what is clearly a growing phenomenon.

Thursday, January 2, 2020

The Night Of The Hallway - 776 Words

The lights in the hallway were quite luminescent, highlighting Vanessa’s obvious lack of clothing. Still in a mesmeric-like state (similar to a daze when one daydreams), Vanessa drifted towards the nearest theater, entering without considering the negative repercussions of being caught. The theater was pitch-black, as Vanessa crept her way forward, approaching the end of the barrier that separated the entrance from the seating area. Before Vanessa turned the corner, possibly exposing herself to a crowd unknown movie patrons (albeit, with limited light) — BOOM! — a massive explosion from the movie startled her. She looked around furiously (regaining control over her consciousness), frantically attempting to discern what had just happened. Before identifying the source of the sound, she realized with great remorse of her current dilemma. â€Å"Why am I NAKED!?† she thought, fidgeting with fear. â€Å"I–I have to get out of here — right now!† Using her arms to shield her breasts, Vanessa made her way back towards the entrance (her only exit), nervously contemplating her next move. Before making further progress, with great dismay, the door began to swing open — people were coming! Vanessa was startled with her awful luck, wondering if destiny wanted her to be exposed in the most embarrassing of circumstances. Determined not to accept her fate, Vanessa pivoted on her back foot, dropped her hands from their protective position, and started to sprint down the aisle. UponShow MoreRelatedMy First Experience With Supernatural World967 Words   |  4 Pagespoint the problem. 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