ENHANCEMENT OF POWER SYSTEM SECURITY USING PSO-NR OPTIMIZATION TECHNIQUE

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1 International Journal of Electrical Engineering & Technology (IJEET) olume 7, Issue 1, Jan-Feb, 2016, pp.35-44, Article ID: IJEET_07_01_004 Available online at ISSN Print: and ISSN Online: Journal Impact Factor (2016): (Calculated by GISI) IAEME Publication ENHANCEMENT OF POWER SYSTEM SECURITY USING PSO-NR OPTIMIZATION TECHNIQUE P. Sobha Rani Associate. Professor, Department of E.E.E Lakireddy Balireddy College of Engineering, Mylavaram, Andhra Pradesh K.S.L. Lavanya Asst. Professor, Department of E.E.E, Lakireddy Balireddy College of Engineering, Mylavaram, Andhra Pradesh ABSTRACT Maintaining power system security is one of the challenging tasks for the power system engineers. The security assessment is an essential task as it gives the knowledge about the system state in the event of a contingency. Contingency analysis technique is being widely used to predict the effect of outages like failures of equipment, transmission line etc., and to take necessary actions to keep the power system secure and reliable. The off line analysis to predict the effect of individual contingency is a tedious task as a power system contains large number of components. Practically, only selected contingencies will lead to severe conditions in power system. The process of identifying these severe contingencies is referred as contingency selection and this can be done by calculating performance indices for each contingencies. This paper presents enhancement of power system security using PSO-NR optimization technique. The efficiency of proposed algorithm is illustrated by carrying simulation studies on IEEE 14 bus system.this analysis reveals that the proposed algorithm is quite simple and efficient for solving OPF problem. Key words: Contingency, Optimal Power Flow (OPF), Particle Swarm Optimization (PSO), Severity Index. Cite this Article: P. Sobha Rani and K.S. Lavanya, Enhancement of Power System Security Using PSO-NR Optimization Technique. International Journal of Electrical Engineering & Technology, 7(1), 2016, pp editor@iaeme.com

2 P. Sobha Rani and K.S. Lavanya 1. INTRODUCTION A reliable, continuous supply of electrical energy is essential part of today s complex societies. In recent years the power systems are pushed to operate closer to their limits due to the combination of increased energy consumption and various kinds of obstructions to extension of existing transmission system. A power system is said to be secured when it is free from danger or risk. Security is ability of the system to withstand any one of the pre-selected list of contingencies without any consequences. Conventional methods [5-7] for contingency analysis involve load flow analysis which is an iterative method. arious methods like AC load flow and several performance index (PI) based methods are used for power system contingency analysis [8]. In conventional methods a power flow solution is required at each iteration, which is again an iterative method itself. Therefore these methods are not suitable for online applications due to the large computation time. All these approaches involve a huge number of AC load flow calculations to determine the bus voltages and line flows for each contingency [3]. It is a challenging task for today s high speed computers and efficient algorithms. Another difficulty is that contingency analysis always uses approximate fast converging load flow algorithms such as Fast Decoupled load flow analysis which has poor convergence characteristics when dealing with heavily loaded power systems. There are other simple techniques such as most popular DC load flow analysis. The results are acceptable when compared with standard AC load flow method; however it can only provide the Real Power (MW) flow under each contingency. Therefore voltage violations and line over loads due to excessive Reactive Power (ar) flows cannot be detected using this method. Distribution factors and sensitivity analysis, another method based on linear model can also be used for this purpose but this method cannot provide accurate solution for a large power system due to its nonlinearity. In the past few decades, many stochastic optimization methods have been developed [9-10], such as Genetic Algorithms (GA) [4], Evolutionary Programming (EP) [12], and Evolution Strategies (ES). Their applications to global optimization problems become attractive because they have better global search abilities over conventional optimization algorithms. Particle Swarm Optimization (PSO) is a newly proposed population based stochastic optimization algorithm which was inspired by the social behaviours of animals such as fish schooling and bird flocking [9-10]. This paper presents PSO-NR method for removing line overloads under single line contingencies. The organization of this paper is as follows. Section II describes contingency analysis. Section III contains Problem formulation of OPF. Section I describes overview of Particle Swarm Optimization. Section presents the results of the simulations system. 2. CONTINGENCY ANALYSIS Contingency Analysis (CA) is one of the "security analysis" applications in a power utility control center that differentiates an Energy Management System (EMS) from a less complex SCADA system. Its purpose is to analyze the power system in order to identify the overloads and problems that can occur due to a "contingency". Contingency analysis is abnormal condition in electrical network. It put whole system or a part of the system under stress. It occurs due to sudden opening of a transmission line, Generator tripping, Sudden change in generation, Sudden change in load value. Contingency analysis provides tools for managing, creating, analyzing, and reporting lists of contingencies and associated violations editor@iaeme.com

3 Enhancement of Power System Security Using PSO-NR Optimization Technique CA is used as a study tool for the off-line analysis of contingency events, and as an on-line tool to show operators what would be the effects of future outages. Security is determined by the ability of the system to withstand equipment failure. Weak elements are those that present overloads in the contingency conditions (congestion). Standard approach is to perform a single (N-1) contingency analysis simulation. A ranking method will be demonstrated to prioritize transmission planning. CA is therefore a primary tool used for preparation of the annual maintenance plan and the corresponding outage schedule for the power system. : 2.1. Contingency Definition Contingency definition involves preparing a list of probable contingencies Contingency Ranking Contingency Ranking in descending order is obtained according to the value of a scalar index, normally called as severity index or performance index (PI). PI is calculated using the conventional load flow algorithm for individual contingency in off line mode. Based on the values obtained contingencies are ranked in a manner where highest value of PI is ranked first Contingency Selection Contingency selection process consists of selecting the set of most probable contingencies in; they need to be evaluated in terms of potential risk to the system Contingency evaluation Finally, the selected contingencies are ranked in order of their severity, till no violation of operating limits is observed. 3. FORMULATION OF OPTIMAL POWER FLOW PROBLEM The OPF problem is to optimize the steady state performance of a power system in terms of an objective function while satisfying several equality and inequality constraints. Mathematically, the OPF problem can be formulated as given: Min F(x, u) (1) Subject to g(x, u) = 0 (2) h(x, u) 0 (3) Where x is a vector of dependent variables consisting of slack bus power, load bus voltages, generator reactive power outputs Q and the transmission line loadings l L S. Hence x can be expressed as given: T X [ PG,...,...,... ] 1 L 1 L Q NL G Q 1 G S NG l Sl nl (4) Where NL, NG and nl are number of load buses, number of generators and number of transmission line respectively. u is the vector of independent variables consisting of generator voltages G, generator real power outputs P G except at the G 37 editor@iaeme.com

4 P. Sobha Rani and K.S. Lavanya slack bus P, transformer tap settings T, and shunt AR compensationsq.hence u G 1 can be expressed as given: U T [ G... G, P...,...,... NG G P 2 G T T NG NT QC Q 1 C 1 1 NC Where NT and NC are the number of the regulating transformers and shunt compensators respectively. F is the objective function to be minimized, g is the equality constraints that represent typical load flow equations and h is the system operating constraints Objective Function The severity of a contingency to line overload may be expressed in terms of the Severity Index, which express the stress on the power system in the post contingency period. In order to evaluate the security of the power system network a Severity Index was proposed. The objective function in the proposed OPF was selected as the minimization of the proposed Severity Index. By minimizing the value of Severity Index, it can observe an enhancement in the system security [3].For example, in order to determine the degree of L. m n ] C (5) SI S S S max max m,n ε NB (6 ) Objective function F =min ( SI ) (7) Where SIthe Severity Index of line overloads, S is the overload flow on transmission line, is the rated flow on transmission line and NB is the Set of overloaded lines Constraints The OPF problem has two categories of constraints Equality Constraints These are the sets of nonlinear power flow equations that govern the power system, i.e., P P Dm l n1 m n Y cos( ) 0 m n (8) Q Q Dm l n1 m n Y sin( ) 0 m n Where P and Qare the real and reactive power outputs injected at bus- m respectively, the load demand at the same bus is represented by P Dm andq Dm, and elements of the bus admittance matrix are represented by Y and. (9) 38 editor@iaeme.com

5 Enhancement of Power System Security Using PSO-NR Optimization Technique Inequality Constraints These are the set of constraints that represent the system operational and security limits like the bounds on the following Generation constraints Generator voltages, real power outputs, and reactive power outputs are restricted by their lower and upper limits as follows: P min Q min max m=1,, NG (10) P P, m=1,, NG (11) Q Q, m=1,, NG (12) max Where NG: number of generators Transformer constraints Transformer tap settings are bounded as follows: min max Tm Tm Tm, m=1,, NT (13) Where NT: number of regulating transformer Shunt AR constraints Shunt AR compensations are restricted by their limits as follows: m=1,, NSC (14) Where NSC: number of Shunt ar Compensators Security constraints These include the constraints of voltages at load buses and transmission line loadings as follows:, m=1,, NL (15) Where NL: number of load buses Transmission lines loading, m=1,, nl (16) Where nl: number of Transmission lines 4. OERIEW OF PSO This is a population based optimization method first proposed by Kennedy and Eberhart in 1995 inspired by social behaviour of bird flocking or fish schooling[11]. The PSO as an optimization tool provides a population based search procedure in which individuals called particles change their position with time by flying around in a multi dimensional search space until a relatively unchanging position has been encountered, or until computational limitations are exceeded. During flight, each particle adjusts its position according to its own experience(this value is called pbest), and according to the experience of a neighboring particle. (This value is called gbest), made use of best position encountered by itself and its neighbor[13-15]. After finding 39 editor@iaeme.com

6 P. Sobha Rani and K.S. Lavanya the best values the particles updated its velocity and position with the following equations: w k C1 r1 ( pbest si ) C2 r2 ( g s k1 k k i i best i ) (17) w w w w )/( iter )) * iter (18) s max s k1 k k1 i i i Where (( max min max C 1 : Cognition Parameter which represents how much the Particle trust its own past experience; C 2 : Social Parameter which represents how much the particle trust the swarm; r 1,r 2 : Random Numbers; w : Inertia Weight; k i : The elocity of the agent i in k th iteration; k 1 i : elocity of agent i at (k+1)th iteration k s i : Current Position of agent i at kth iteration 5. PSO-NR BASED HYBRID METHOD Basically, the hybrid method involves two steps. The first step employs NR to solve OPF approximated as a continuous problem and introduced into the initial populations of PSO. The second part uses PSO to obtain the final optimal solution. In initial population, all individuals (obtained from NR) are produced randomly. The main reason for using the NR is that it is often closer to optimal solutions than other random individuals. In the hybridization of NR and PSO, the NR generates best initial solutions from random initial solutions and PSO evaluate them by solving the OPF, which yields to the global optimal solutions for control variables. The implementation steps of the proposed PSO-NR based algorithm can be written as follows Step 1: Input the system data for load flow analysis Step 2: Assume several contingencies Step 3: At the generation Gen =0, set the simulation parameters of PSO-NR parameters and randomly initialize k individuals within respective limits and save them in the archive. Step 4: For each individual in the archive, run power flow to determine load bus voltages, angles, load bus voltage stability indices, generator reactive power outputs and calculate line power flows. Step 5: Evaluate the penalty functions Step -6: evaluate the objective function values and the corresponding fitness values for each individual Step 7: Find the generation local best xlocal and global best xglobal and store them Step 8: Increase the generation counter Gen = Gen+1. Step 9: Apply the pso-nr operators to generate new k individuals (19) 40 editor@iaeme.com

7 Enhancement of Power System Security Using PSO-NR Optimization Technique Step 10: For each new individual in the archive, run power flow to determine load bus voltages, angles load bus voltage stability indices, generator reactive power outputs, calculate line power flow Step 11: Evaluate the penalty functions Step 12: Evaluate the objective function values and the corresponding fitness values for each new individual. Step 13: Apply the selection operator of PSO-NR and update the individuals. Step 14: update the generation local best xlocal and global best xglobal and store them. Step 15: If one of stopping criterion have not been met, repeat steps Else go to step 16 Step 16: checking the limit violation for security constraints. If iterations reached to its max value then go to else go to step 2. Step 17: Stop 6. SIMULATION &RESULTS The proposed approach has been tested on the standard IEEE 14-bus test system as shown in fig1. The system has five generators at buses 1, 2, 3, 4, 5, and three transformers with off-nominal tap ratio in lines 6-4, 7-9, 7-8. PSO parameters used for simulation are summarized in TABLE 1. Figure 1 Single line diagram of IEEE 14 bus system Table 1 Optimal Parameter Settings for PSO Parameter alue Population size 20 Number of iterations 150 Cognitive constant,c 1 2 Social constant,c 2 2 Inertia weight,w editor@iaeme.com

8 P. Sobha Rani and K.S. Lavanya TABLE 2 gives the details of line outage ranking using severity index. When the line outage is between buses 1-2, without PSO-NR condition, it is observed that the lines 2-6, 4-12 are overloaded with line flows MA and 41.87MA respectively against their line flow limits 65 MA &32 MA. In order to rectify the problem of overflows in lines PSO-NR has been implemented, and then the line flow limits that are violated under without PSO-NR condition is rectified with the values MA and MA respectively. It is observed that severity index is reduced by using PSO-NR technique when compared with out PSO-NR severity index. TABLE 3 presents the setting of control variables for IEEE 14-bus system for without PSO-NR case and with PSO NR at different single line outages. From the results, it is observed that all the control variables are within limits and lines are operating within the specified line limits by the application of PSO-NR based OPF algorithm under the occurrence of various severe network contingencies. Line outage between buses Control variables Over loaded lines Table 2 Line Outage Ranking Using Severity Index Line flow limit (MA) Line flow (MA) Without PSO-NR With PSO - NR Severity Index SI Without PSO-NR With PSO- NR Ranking Without PSO- NR Table 3 Control ariables Setting For IEEE 14-Bus System 1-2 outage 1-8 outage 2-3 outage 2-6 outage With Without With Without With Without PSO- PSO- PSO- PSO- PSO- PSO- NR NR NR NR NR NR P P P P P T T T With PSO- NR 42 editor@iaeme.com

9 Enhancement of Power System Security Using PSO-NR Optimization Technique CONCLUSION This paper presents an improved, efficient and reliable PSO-NR algorithm for solving Optimal Power Flow problem under occurrence of various single line contingencies. The proposed method is tested on IEEE-14bus system and the simulation results are reported. From the results it can be concluded that Severity index, that is calculated indicates how much severe a possible line outage is and the severity of each line outage in the system. The severity index with highest value indicates the severity of that particular line outage and also indicates that it has got maximum chances of making system parameters to operate beyond the operating limits. PSO-NR based Optimal Power Flow algorithm mitigates severity index and shows better performance under critical conditions. The results show the effectiveness and robustness of the proposed algorithm in order to solve OPF problem. REFERENCES [1] Atiya naaz, L.Sayyed, Pramod M.Gadge and Ruhi Uzma Sheikh, Contingency Analysis and Improvement of Power System Security by locating Series FACTS Devices, TCSC and TCPAR at optimal location, IOSR Journal of Electrical and Electronics Engineering e-issn: , P-ISSN: , pp [2] M.R.Iravani and D.Maratukulam, Review of semiconductor controlled (static) phase shifters for power system applications, IEEE Trans. on Power Systems, vol. 9, no. 4, pp , [3] Wei Shao and ijay ittal, LP-Based OPF for Corrective FACTS Control to Relieve Overloads and voltage violations, IEEE Trans. on Power Systems, vol.21, no.4, November [4] M. Huneault and F.D. Galiana, A Survey of the Optimal Power Flow Literature, IEEE Trans. on Power Systems, o1.6, No. 2, May [5] James A.Momoh, M.E.EI-Hawary and Rambabu Adapa, A Review of Selected Optimal Power Flow Literature to 1993 Newton Linear Programming and Interior Point Methods, IEEE Trans. on Power Systems, vol.14, no.1, February [6] Sergio Granville and CEPEL, Optimal Reactive Dispatch through Interior Point Method, IEEE Trans.on Power Systems, vol.9, no.1, February [7] Ding Xiaoying, Wang Xifan, Song Yonghua and Geng Jian, The Interior Point Branch and Cut method for Optimal Power Flow, IEEE 2002; [8] Brian stott, Ongun alsac, and Alcir j.monticelli, Security Analysis and Optimization, Proc. IEEE, vol. 75, no.12, pp , December [9] R..Amarnath and Dr.N..Ramana, State of Art in optimal power flow solution methodologies, Journal of theoretical and Applied Information Technologies 31 st August 2011.vol 30, no 2 [10] Anastasias, G.Bakirtzis, pandel N, Biskas and Christoforos, Optimal power flow by enhanced Genetic Algorithm, IEEE Trans on Power Systems vol 17, No 2 May [11] M.Sailaja Kumari, G.Priyanka and M.Sydulu, Comparison of Genetic Algorithms and Particle Swarm Optimization for Optimal Power Flow Including FACTS devices, IEEE 2007; [12] Yuryevich and Jawing KP, Evolutionary programming based optimal power flow algorithm, IEEE Trans Power Syst 1999; 14(4): editor@iaeme.com

10 P. Sobha Rani and K.S. Lavanya [13] Bhavna Sharma and Mandaree panjit, Security Constrained Optimal Power Flow employing Particle Swarm Optimization, IEEE 2012, [14] Yamille del valle,ganesh Kumar and enayagamoorthy, Particle Swarm Optimization :Basic concepts,ariants and Applications in Power Systems, IEEE Trans on Evalutionary Computation,vol 12,no 2,April [15] Hc Leung and Dylan Dah-chuan Lu, Particle Swarm Optimization for OPF with consideration of FACTS devices, IEEE 2011: [16] R.Behera, C.R. Dash and Dillip Khamar, GA Based Optimal Power Flow Using Fact Device. International Journal of Electrical Engineering & Technology, 5(12), 2014, pp [17] D. Pattanayaka, M. Basub,R. N. Chakrabartic, Multi-Objective Differential Evolution for Optimal Power Flow. International Journal of Electrical Engineering & Technology, 3(1), 2012, pp AUTHOR INFORMATION P.SOBHA RANI is currently working as Assoc. Professor in the Department of Electrical &Electronics Engineering, Lakireddy Balireddy college of Engineering, Mylavaram, Andhra Pradesh, India. Her areas of interest are Distribution systems, Power system security and distributed generation. K.S.L.LAANYA is currently working as Assistant Professor in the Department of Electrical &Electronics Engineering, Lakireddy Balireddy college of Engineering, Mylavaram, Andhra Pradesh, India. She obtained M. Tech from P..P Siddhartha Institute of Technology, ijayawada, Andhrapradesh, Her areas of interest are Power System Security, OPF techniques and FACTS