Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays

Round 1

Reviewer 1 Report

Research presented in this article provides some useful information regarding optimisation issues in Optimal Coordination of Directional Overcurrent Protection Relays applications issues. However some parts are missing from this research. Here are the list of the questions:

1. What will happen in this approach of coordination if there are several sources present in the power grid? In all cases presented in the article is always one.
2. Is this algorithm (or results) applied to the real case and tested in "real world"?
3. Comparison of the simulated cases and real system will be of great help to see it this approach is right one. Is this possible to the authors to do?
4. Some figures ( 3, 6, 10, 12) are disproportional in comparison to the rest of the document. Can this be corrected?
5. Test systems data are missing (if someone would like to repeat the experiment) and should be included in Appendix for each test system. 

Author Response

 

Response to reviewer 1

 

 

 

 

Dear Reviewer:

 

Thank you very much for your kind words and precious recommendations to modify the manuscript. We have made the amendments according to your comments. Our point-By-point responses for each comment are below.

The modifications are highlighted in red colour in the revised manuscript.

 

 

Comment 1: What will happen in this approach of coordination if there are several sources present in the power grid? In all cases presented in the article is always one.

 

Reply:

we appreciate the reviewer comment for this useful comments. In revised version of our manuscript, we have implement our proposed algorithm for multiple sources in the power grid to validate the performance of our proposed algorithm. In case of several sources the proposed HPSO performing outstandingly, minimize the total operating time to optimum value, and give a refined solution as compared to other stat of the art algorithm. From the simulation result it was observed that the proposed HPSO is also useful tool to deal with the coordination problem in case of multiple DG present in the power grid

IEEE 3-bus system uses three sources. Case 5 in section 4.5 added in the manuscript.

“A single line diagram of power distribution system with six-overcurrent protection relays, three buses, three AC sources, and three lines are shown in figure 14. Four directional overcurrent relays are denoted as R1 to R6, current transformer ratio between these relays are given in table 14. The relationship between primary and backup relays and their fault currents are given in table 15. The PS is kept constant at 1.5 and the TMS lower and upper bound are set to 0.1 and 1.1, respectively. The coordination time interface (CTI) value is chosen to be 0.3 s. The optimized TMS of proposed PSO and HPSO are given in Table 16.”

 

 

Figure 14. IEEE 3-bus system   

Table 14. CT ratios of the relay (IEEE 3-bus system)

Relay	CT Ratio
1, 3	300:5
2, 3, 5	200:5
6	400:5

Table 15. Relationships between primary and backup relays and fault currents (IEEE 3-bus)

Primary Relay	Fault Current (A)	Backup Relay	Fault Current (A)
1	1978.9	5	617.22
2	1525.7	4	145.34
3	1683.9	1	384
4	1815.4	6	545
5	1499.66	3	175
6	1766.3	2	466.17

 

Table 16. Comparison of the optimized TMS with other techniques (case 5).

TMS	WOA [2]	HWOA [2]	HPSO
TMS (Relay 1)	0.050	0.050	0.1065
TMS (Relay 2)	0.050	0.050	0.1597
TMS (Relay 3)	0.05553	0.050	0.1078
TMS (Relay 4)	0.050	0.050	0.10
TMS (Relay 5)	0.0710	0.0612	0.1631
TMS (Relay 6)	0.1587	0.8065	0.1113
	1.5262	1.5029	1.4424

Table 16, shows the comparison of the proposed algorithm with other published technique described in the literature. The proposed HPSO method works and performs better in comparison with other evolutionary techniques. The graphical illustration of the optimized net gain in time (sec) compared with the proposed HPSO is shown in figure 15 that shows the superiority of HPSO over other methods described in the literature. The graph of the convergence characteristics of the IEEE 3-bus system is shown in Figure 16, which shows that the convergence is faster and the minimum operating time to trace a fault is achieved in fewer iterations.

 

Figure 15. Comparison analysis of HPSO with other algorithms (case 5).

 

Figure 15. Convergence characteristic for IEEE 3-bus system.

 

 

Comment 2: Is this algorithm (or results) applied to the real case and tested in "real world"?

  Reply:

We totally agree with this comment of the reviewer that there is no experimental or real time hardware in the loop simulation in this manuscript. As, we know that Power System is a very large and complex interconnected system. It is very difficult to test the proposed technique on a live complex system. The only way to perform such activities in power systems is to have a scaled down experimental laboratory test bed. Power Systems laboratory test beds for transient stability simulations are not commercially available. The authors are also interested to develop their own test bed (in the SoftPower and Power System Research Group, Electrical Engineering Deptt china) equipped with relays, and recloser to test advanced control strategies in real-time. Hence, the current research work will be extended in the direction of real-time Power System simulation test bed in the laboratory. The proposed scheme suggested by the authors seems (validated by the simulations) has globally optimum and better solution. So, the authors believe that the proposed technique will equally perform better on a scaled down laboratory test system for the disturbances subjected to the test system

 

Comment 3: Comparison of the simulated cases and real system will be of great help to see it this approach is right one. Is this possible to the authors to do?

 

Reply:

we know that Power System is a very large and complex interconnected system. It is very difficult to test the proposed technique on a live complex system. The only way to perform such activities in power systems is to have a scaled down experimental laboratory test bed. Power Systems laboratory test beds for transient stability simulations are not commercially available. The authors are also interested to develop their own test bed.

The authors are also interested to develop their own test bed (in the SoftPower and Power System Research Group, Electrical Engineering Deptt china) equipped with relays, and reclose to test advanced control strategies in real-time. Hence, the current research work will be extended in the direction of real-time Power System simulation test bed in the laboratory. The proposed scheme suggested by the authors seems (validated by the simulations) has globally optimum and better solution. So, the authors believe that the proposed technique will equally perform better on a scaled down laboratory test system for the disturbances subjected to the test system.

In context to following references, the researchers had also done the silico work. The experimental validation for the proposed manuscript is at a time costly [2, 3, 4, 6, 10, 15, 20, 22, 27, 31, and 39].

Comment 4: Some figures ( 3, 6, 10, 12) are disproportional in comparison to the rest of the document. Can this be corrected?

 

Reply:

Very good suggestion raises, modifications done in all the figures.         

Case 1: HPSO compared with the other algorithms and their difference shows the net gain.

Figure 3. Comparison analysis of HPSO with other algorithms (Case 1).

 

 

Case 2:

Figure 6. Comparison analysis of HPSO with other algorithm (Case 2).

 

 

 

 

Case 3:

 Figure 10. Comparison analysis of HPSO with other algorithm (Case 3).

 

 

Case 4:

Figure 12. Comparison analysis of HPSO with other algorithm (Case 4).                     

 

 

Case 5:

Figure 15. Comparison analysis of HPSO with other algorithms (case 5).

 

Comment 5: Test systems data are missing (if someone would like to repeat the experiment) and should be included in Appendix for each test system.

 

Reply:

System data can be easily taken from reference [9, 16, 20, 43, 44, and 45]. Also included in each test case.

Author Response File: Author Response.pdf

Reviewer 2 Report

Authors have used particle swarm optimization(PSO) with simulated annealing(SA) algorithm to optimize the operating times of relays used in directed over current protection systems. They have simulated the algorithm in MATLAB and show that it provides the lowest time of operation compared to the earlier state-of-the-art results from previous works. 

 

In general the idea is valuable but the paper needs to improve quite a bit before it is ready for publication.

 

English writing has to improve. Please carefully proof-read spell check to eliminate grammatical errors.

 

Line 14-15: “you state that coordination of relays is an optimization problem”. Can you explain clearly in paper how and why it is an optimization problem? Using a diagram with relay configuration will be useful to explain this.

 

Line 28: use of term “anomalies” is incorrect. Tree falling is not an anomaly. It was an accident. 

Line 31: similar to above comment. You can not “avoid” accidents. You have to rectify the accident and behave safely.

 

Line 59 to 61: can you provide a reference for the statement in this line?

 

Clearly state the objectives and contribution of this paper. Explain how this work is significantly different from previous works.

 

I suggest making a table to clearly explain the basic concepts of relay operation such as Plug setting multiplier, Time setting multiplier, current transformer, pickup current, etc. Using a diagram to explain these concepts will be useful.

 

Figure 1: why is “Stopping criteria met” a conditional diagram while it has only “Yes” outgoing branch?

 

In general, what is the outcome of your research? How are you using the output of the algorithm in your DOPR system? And why is it important to achieve overall  sub sec improvement in relay operation latencies? All these need to be answered. 

 

You are repeating the same reason in all scenarios as “controlling for local optimum and provides a lower total operating time”. This isn’t enough. Please elaborate more on why PSO+SA is performing slightly better compared to other optimization methods.

 

The Conclusions section is pretty small. You should focus more on how PSO is useful for DOPR use cases rather than focusing on the PSO algorithm itself.

Author Response

 

Response to reviewer 2

 

 

 

 

Dear Reviewer:

 

Thank you very much for your kind words and precious recommendations to modify the manuscript. We have made the amendments according to your comments. Our point-By-point responses for each comment are below.

The modifications are highlighted in red colour in the revised manuscript.

 

 

Comment 1: Line 14-15: “you state that coordination of relays is an optimization problem”. Can you explain clearly in paper how and why it is an optimization problem? Using a diagram with relay configuration will be useful to explain this.

 

Reply:

Any problem in which a certain objective need to be obtained while satisfying its necessary conditions can be termed as an optimization problem. Normally, this objective may be an error or cost function, which needs to be minimized, and the conditions are the constraints that must be satisfied. Mathematically, an optimization problem can be formulated as shown in reference [a]. In this case our objective function is minimization problem and can be formulated as 

min(z)=

       Constraints:

  

 

 

Relay configuration figure is shown below also added in manuscript as figure 1. Explanation of this figure is given in question 6.

 

2009. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2009.

 

Comment 2: Line 28: use of term “anomalies” is incorrect. Tree falling is not an anomaly. It was an accident.

  Reply:

Very well noted and suggested comment. Modifications done in the manuscript as changed the word anomaly with accident.        

 

Comment 3: Line 31: similar to above comment. You cannot “avoid” accidents. You have to rectify the accident and behave safely.

 

Reply:

Very well noted and suggested comment. Modifications done in the manuscript as changed the word anomaly with accident.                

 

Comment 4: Line 59 to 61: can you provide a reference for the statement in this line?

 

Reply:

Reference added as reference [16].

       

Comment 5: Clearly state the objectives and contribution of this paper. Explain how this work is significantly different from previous works.

 

Reply:

We appreciate the reviewer for his concerns about the contribution of the work.

Reply and Action: In this paper, the optimal coordination of DOPRs was determined by hybrid version of particle swarm optimization namely as HPSO deployed in a multi-loop power system. The hybridization is done by introducing simulated annealing (SA) in original PSO to avoid being trapped in local optima and successfully search for global optimum solution. The suggested HPSO has extraordinary exploration competency and speed as compared to other meta-heuristic techniques; this characteristic makes the population members of HPSO more discriminative when searching for the optimal solution compared to other meta-heuristic algorithms. The primary aim of our proposed HPSO is to determine the optimal values of TMS to minimalize the operational period of DOPRs with respect to backup and relay setting restrictions.

The coordination of directional overcurrent relay more specifically primary directional overcurrent relay is known to be operational time-intensive system, owing to the non-optimal execution of time multiplier setting. Indeed, industry always focuses towards optimizing the directional overcurrent relay coordination in order to the avoid mal-operation and mal-functioning of the system during troubleshooting, which ultimately will ensure the minimum baring to the healthy portion of the system.

However, the directional overcurrent time coordination in power distribution network is a major concern of protection engineer and is considered a challenging exercise owing to the highly constrained objective function. To address this issue, a lot of research works [1-5], and [9-45], have been continuously explored to the optimization of directional overcurrent relay coordination problem. Directional overcurrent relay based systems for finding the improved optimal coordination of directional overcurrent relay in a robust and efficient way, as already mentioned in the introduction of the manuscript.

We believe these overcurrent relay coordination optimization works have made a clear contribution and novelty, while most of them just utilized or applied the existing or emerging optimization methodologies from the literature rather than developing a new methodology itself. In this context, this paper also aims to explore the ways of improving and finding the optimal coordination of overcurrent relay through a newly developed hybrid swarm based optimization approach naming as “HPSO”. A typical way most widely used to evaluate optimization algorithms is just to try them with same basis, and highlight its potential superiority for the particular problem based on the observation results.

        

Comment 6: I suggest making a table to clearly explain the basic concepts of relay operation such as Plug setting multiplier, Time setting multiplier, current transformer, pickup current, etc. Using a diagram to explain these concepts will be useful.

Reply:

Figure 1 added to show the schematic outline for DOPR coordination.

Figure 1, shows the DOPR coordination scheme in a power supply system which consists of pickup current (Ip), plug setting multiplier (PSM), time multiplier setting (TMS), and current transfer ratio (CTR) etc. The current at which the relay starts its operation expressed as the relay pickup current (Ip ). Plug setting multiplier (PSM), is the ratio between the fault current (If) and the pickup current (Ip ). The pickup current is the multiple of the rated secondary current of the CT and the current setting called the current transfer ratio (CTR). To adjust the operating time in the relay, it is necessary to adjust the setting of the time multiplier (TMS) [30]. The constraints of the DOPR are the coordination constraints and the characteristic constraints and the objective of this research is the optimal coordination of the DOPR.

 

Comment 7: Figure 1: why is “Stopping criteria met” a conditional diagram while it has only “Yes” outgoing branch?

Reply:

Very helpful issue raised. Modifications done according to reviewer instructions.

 

 

 

 

 

 

 

 

 

Comment 8: In general, what is the outcome of your research? How are you using the output of the algorithm in your DOPR system? And why is it important to achieve overall sub sec improvement in relay operation latencies? All these need to be answered.

Reply:

We appreciate the reviewer effort for this useful comment

The HPSO algorithm was used to assess the DOPR coordination problem. The suggested algorithm has a high search capability and convergence speed, and these distinctive features make the swarm agents of the HPSO more discriminative in finding the optimum solution. Different optimization algorithms as shown in the literature have also evaluated the case studies presented in this paper, and an improved optimal solution was observed from the proposed HPSO algorithm compared to these other algorithm options. The DOPR coordination problem is basically a highly constrained optimization problem. As the HPSO can solve constrained and unconstrained optimization problems, the relay coordination problem has been converted into an optimization problem by defining a new objective function and by using the boundaries on the TMS and PS (and boundaries on the relay operating time) as the limits of the variables. A systematic procedure for converting a relay coordination problem into an optimization problem has been developed in this paper. A program has been developed in MATLAB for finding the optimum time coordination of DOPRs using the HPSO method. The program can be used for setting the optimum time coordination of DOPRs in a system with any number of relays and any number of primary-backup relationships. The TDS, PS and total operating time of relays obtained for all case studies by the proposed HPSO ensured that the DOPRs would activate in the minimum possible amount of time for a fault at any point in the system. However, if the number of relays is increased, the nature of the highly constrained problem becomes more distinct. Therefore, an accurate and optimum relay coordination minimizes the total operating time as well as reduces and limits the damage produced by the fault. Unwanted tripping of the circuit breakers can also be bypassed by this method. The convergence characteristic graphs obtained during simulations show that the convergence is faster and obtains a superior solution for the objective function in fewer iterations. The HPSO algorithm is superior to the [9, 16, 20, 43, 44, and 45] algorithms, as shown in Table 3, 6, 10 and 12, HPSO receives net gains of 0.06 s and 1.59 s versus GA, 0.001 s, 1.45 s, and 0.011 s versus SM, DSM, and CPSO, respectively in case 1. For case 2, considering net gains of 0.05 s versus TPSM and 0.61 s, 0.51 s versus FA and CFA algorithms, respectively. . For case 3, a net gain of 0.18 s was obtained over the SM. In case 4, HPSO earns high net gain versus CGA, FA, and CFA of 4.02 s, 4.39 s, and 2.53 s, respectively. From tables 3, 5, 9, 13, and 16, it shows that the proposed method is superior to the recent published techniques mentioned in the literature in terms of the quality of the solution, convergence, and minimization of the objective function to the optimum value. The suggested technique furthermore addressed the weaknesses of the already published algorithms.

 

Comment 9: You are repeating the same reason in all scenarios as “controlling for local optimum and provides a lower total operating time”. This isn’t enough. Please elaborate more on why PSO+SA is performing slightly better compared to other optimization methods.

Reply:

Modifications done in all the four cases, such as

For case 1: “Moreover, the HPSO has the ability of random and large-scale search of particle swarm optimization, and provides a lower total operating time ( )”.

For case 2: “The suggested HPSO has extraordinary exploration competency and speed as compared to other meta-heuristic techniques; this characteristic makes the population members of HPSO more discriminative when searching for the optimal solution compared to other meta-heuristic algorithms”.

For case 3: “The proposed algorithm solved the random initial solution drawback compared with SA algorithm. It helped to find the optimal solution for each subcarrier and to find to minimize the total operating time ( ). ”

For case 4: “The proposed algorithm HPSO combines the exploration ability of PSO with the exploitation ability of SA, to avoid being trapped in local optima and successfully search for global optimum solution. The suggested HPSO has extraordinary exploration competency and speed as compared to other meta-heuristic techniques; this characteristic makes the population members of HPSO more discriminative when searching for the optimal solution compared to other meta-heuristic algorithms.”

Comment 10: The Conclusions section is pretty small. You should focus more on how PSO is useful for DOPR use cases rather than focusing on the PSO algorithm itself.

Reply:

Very helpful issue raised. Modifications done according to reviewer instructions.

In this paper, the HPSO algorithm is suggested to exactly and progressively evaluate the constraints of various DOPR models. The HPSO is analysed over parameter identification issues of DOPR models. The simulation results demonstrate that HPSO has a better performance in terms of precision and consistency compared with other methods in the literature. The results obtained by HPSO technique effectively minimize all the five models of the problem. The performance of HPSO can be seen from the minimum function estimation acquired by the HPSO to reach the optimal value compared to other algorithms from the literature. In case 1, the objective function value is minimized up to optimum value by HPSO and gives an advantage in total net gain in time of 0.06 s, 1.59 s, 0.001 s, 1.45 s, and 0.011 s over GA1, GA2, SM, DSM, and CPSO. In case 2, the HPSO gives a total net gain in time of 0.05 s, 0.61 s, 0.51 s versus TPSM, FA and CFA. In case 3, 0.18 s net gain was obtained over the SM. In case 4, the HPSO gives a total net gain in time of 4.02 s, 4.39 s, and 2.53 s over the CGA, FA, and CFA algorithms. While in case 5, the HPSO gives a to-tal net gain in time of 0.08 s and 0.06 s versus WOA and HWOA, respectively. Thus, HPSO is a hopeful candidate solution for solving the constraint identification problem of DOPR models. In the future work, this technique will be used to solve issues of DOPR and DOPR of higher and more complex case studies in power systems.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Thanks for providing the updated version after addressing previous comments.

Here are my follow up comments:

Authors should choose more appropriate and simple words in their explanation. For example, in conclusion “precision and consistency compared with other methods in Literature.” doesn’t make much sense since they actually evaluate their algorithm for latency rather than precision or consistency.

I applaud the authors for improving the paper based on previous comments. I still think the English language and style has to improve. 

The last part of the “Comparison results analysis” and Conclusions section are almost duplicate. You can de-dup and condense these sections.

Appreciate your response to my comment 5: please find a way to include this explanation in the paper.

Similarly, Appreciate your response to comment 8. Can you include this explanation in the paper? 

Please explain in the paper how this algorithm will be useful in practical power systems which presumably are more complicated than the examples you used in your evaluations. 

Author Response

 

Response to reviewer 2

 

 

 

 

Dear Reviewer:

 

Thank you very much for your kind words and precious recommendations to modify the manuscript. We have made the amendments according to your comments. Our point-By-point responses for each comment are below.

The modifications are highlighted in red colour in the revised manuscript.

 

 

Comment 1: Authors should choose more appropriate and simple words in their explanation. For example, in conclusion “precision and consistency compared with other methods in Literature.” doesn’t make much sense since they actually evaluate their algorithm for latency rather than precision or consistency.

 

Reply:

Modifications done in the sentence. “The simulation results demonstrate that HPSO has a better performance in terms of minimizing operating time compared with other methods in the literature”.

 

Comment 2: The last part of the “Comparison results analysis” and Conclusions section are almost duplicate. You can de-dup and condense these sections.

  Reply:

Conclusion section modified as all the five cases information are available in the comparison section. So removed from there and some modifications done.

 

Comment 3: Appreciate your response to my comment 5: please find a way to include this explanation in the paper.

 

Reply:

Explanation added in the introduction section.

“In this paper, the optimal coordination of DOPRs was determined by hybrid version of particle swarm optimization namely as HPSO deployed in a multi-loop power system. The hybridization is done by introducing simulated annealing (SA) in original PSO to avoid being trapped in local optima and successfully search for global optimum solution. The coordination of primary directional overcurrent relay is known to be operational time-intensive system, owing to the non-optimal execution of TMS. Indeed, industry always focuses towards optimizing the directional overcurrent relay coordination in order to the avoid mal-operation and mal-functioning of the system during troubleshooting, which ultimately will ensure the minimum baring to the healthy portion of the system.”

 

Comment 4: Similarly, Appreciate your response to comment 8. Can you include this explanation in the paper?

 

Reply:

Explanation added in the comparison section.

“The HPSO algorithm was used to assess the DOPR coordination problem. The suggested algorithm has a high search capability and convergence speed, and these distinctive features make the swarm agents of the HPSO more discriminative in finding the optimum solution. Different optimization algorithms as shown in the literature have also evaluated the case studies presented in this paper, and an improved optimal solution was observed from the proposed HPSO algorithm compared to other algorithm. The DOPR coordination problem is a highly constrained optimization problem. A program has been developed in MATLAB for finding the optimum time coordination of DOPRs using the HPSO method. The program can be used for setting the optimum time coordination of DOPRs in a system with any number of relays and any number of primary-backup relationships. ”

       

Comment 5: Please explain in the paper how this algorithm will be useful in practical power systems, which presumably are more complicated than the examples you used in your evaluations.

 

Reply:

 

Some explanation added in the conclusion section.

“We totally agree with this comment of the reviewer that there is no experimental or real time hardware in the loop simulation in this manuscript. As, we know that Power System is a very large and complex interconnected system. It is very difficult to test the proposed technique on a live complex system. The only way to perform such activities in power systems is to have a scaled down experimental laboratory test bed. Power Systems laboratory test beds for transient stability simulations are not commercially available. The authors are also interested to develop their own test bed. The authors are also interested to develop their own test bed (in Xidian University, Electrical and Electronics department, china) equipped with relays, and recloser to test advanced control strategies in real-time. Hence, the current research work will be extended in the direction of real-time Power System simulation test bed in the laboratory. The proposed scheme suggested by the authors seems (validated by the simulations) has globally optimum and better solution. So, the authors believe that the proposed technique will equally perform better on a scaled down laboratory test system for the disturbances subjected to the test system.

 

        

Author Response File: Author Response.pdf